Risk Assessment and Prioritization using Fuzzy FMECA: A Case Study of Dumper Breakdowns

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Raju, M.V. Satish Kumar, GAJANAND GUPTA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4179461/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Sep, 2024 Read the published version in International Journal of System Assurance Engineering and Management → Version 1 posted 5 You are reading this latest preprint version Abstract The production and supply of coal are always prominent issues for thermal power plants due to the high demand for electricity generation. The dump trucks used in open-pit mining serve as a medium to transport the coal. Interrupted services of dump trucks due to breakdown can lead to delayed and incorrect supply to the generating units. To improve the productivity and for the proper delivery of the coal, the downtime should be minimized and the reliability should be maximized. FMECA is one of the prominent reliability modelling techniques that prevent occurrence of sudden failures. In order to overcome the shortcomings of traditional FMECA, fuzzy rule based approach is proposed in this paper to mitigate the failures in dumpers. Fuzzy FMECA helps in resolving the uncertainty and ambiguity in traditional FMECA approach and can work in a better way even using insufficient information when compared with traditional FMECA. Risk Assessment Dumper FMECA Fuzzy FMECA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Introduction Coal mining is the process of extracting coal from the earth’s surface and also from the underground [ 1 ]. Open pit mining is a way where coal is extracted by removing a layer of soil and rock bed [ 2 ]. Different types of machinery and equipment are used in open pit mining for the removal and transport of coal. Bull dozer, dragline, bucket wheel excavator, drill rig, dumpers are some of the equipment used in open pit mining [ 3 ]. In order to meet established production targets, mining organizations require more reliable equipment [ 4 ]. Reliability specifies the overall machine condition and it can be defined as the probabilitythat a device or a system will perform its function satisfactorily for a given time periodwhen used according to stated operating conditions [ 5 ]. Around 12%-15% of manufacturing time iswasted by unplanned maintenance in open pit mining [ 6 ] and also the cost of maintenance is morethan 60% of the operation cost [ 7 ]. Therefore, the reliability of mining equipment is a major problem that needs to be addressed. Failure Mode Effect and Criticality Analysis (FMECA) is a sophisticated technique used in industries to predict and enhance the reliability of equipment. It identifies and prioritizes the failure points of a system/sub-system before they occur. Thisconcept was originated with the US Army in 1949 to improve and inspect the reliability of military systems [ 8 ]. FMECA is proved to be one of the prominent preventive action methodologies which can avoid sudden failures. FMECA uses Risk Priority Number (RPN) which is a measure of the overall risk of a failure mode. RPN is calculated by multiplying three indices i.e., Occurrence (O), Severity (S) and Detection (D) as given in Eq. 1. These three factors are evaluated for each failure mode on a ten-point scale, usually from 10 to 1. Risk Priority Number = \({(\text{O}}_{\text{i}\text{j}})\times {(\text{S}}_{\text{i}\text{j}})\times {(\text{D}}_{\text{i}\text{j}})\) (1) (i = 1,2,3….n & j = 1,2,3….m) Here, \({O}_{ij}{, S}_{ij} and {D}_{ij}\) refers to the occurrence, severity and detection of i th failure mode of j th component. Failure modes with the greatest RPN values are thought to be more significant and should be resolved before those with lower RPN values. One of the effective risk assessment methods that examines probable failures in order to prevent them is traditional FMECA. However, conducting classic FMECA only has a few drawbacks. The list of these negatives is shown below. a. Duplicate RPNs While different O, S, and D rank combinations may provide exactly the same RPN number, their hidden risk implications may vary. RPN values 270, 210, and 168, for instance, can be found in twelve different O, S, and D combinations. Similar rank and RPN values for several clusters may not necessarily reflect the same level of danger. Wasted time and resources may result from this, and in some situations, high-risk failure modes might go undiscovered. b. Gaps in the range The majority of values between 1 and 1000 cannot be formed, hence the RPN values produced by multiplying O, S, and D are not continuous. Only 120 out of 1,000 are RPN numbers, meaning that 88% of the range is unfilled. Therefore, RPN cannot display any integers that are multiples of 11. The multiples of 13, 17, and 19 are likewise disregarded. Following 1000, 900 is the biggest RPN value, followed by 810 and 800. c. Lacks scientific base The mathematical computation for obtaining RPN is debated as it lacks a complete scientific basis. There is no justification for multiplying the O, S, and D parameters to produce the RPN. d. Uncertain information It is challenging to assess the three risk variables O, S, and D properly. The majority of the data in FMECA is frequently ambiguous or questionable. For accurate assessment, language phrases like "medium," "many," or "very high" might be employed. Applying fuzzy logic can be highly advantageous in addressing the limitations of conventional FMECA methods. The inherent uncertainty involved in the decision-making process may be successfully described by using fuzzy theories. This method helps to eliminate the ambiguity and uncertainty seen in conventional FMECA. Fuzzy logic was suggested by Bowles J. B. et al. [ 9 ] as a method for rating the failure scenarios in FMECA. Based on the RPNs acquired through FMECA, Feliks and Bukowski [ 10 ] devised a fuzzy logic approach to evaluate the risk in a system. The fuzzy logic-based FMECA methodology may be streamlined by Lim and Tay [ 11 ] by reducing the number of rules needed to achieve F-RPN. Chin et al. [ 12 ] suggested employing fuzzy FMEA to assess the idea of product development. Using a fuzzy logic model, Liu and Tsai [ 13 ] created preventative strategies for workplace risks in the construction sector. Albeanu G. et al. [ 14 ] developed a methodology using intuitionistic-fuzzy numbers to assess the reliability of wind turbine. Gajanand. G et al. [ 15 ] proposed a study on milling machine using fuzzy logic for reliability-centered maintenance. Renjith V. R. et al. [ 16 ] employed the Fuzzy FMECA approach to address the issues in failure mode prioritization of LNG storage facility. Gajanand G and Mishra R.P. [ 17 ] analysed the criticality of lathe machine using traditional and fuzzy approaches of FMECA. Dejan V. P. et al. [ 18 ] implemented a methodology by interlinking fuzzy sets and statistical methods to analyze the risk level of a system. The present study extends the application of fuzzy logic to FMECA, specifically focusing on prioritizing failure modes. Fuzzy FMECA offers a superior approach that can produce reliable results, even when faced with limited information, in contrast to traditional FMECA. Traditional FMECA The conventional FMECA serves as a means to assess possible failure modes and their underlying causes. Its primary purpose is to prioritize these failure modes and provide recommendations for preventive measures, aiming to avert catastrophic failures and enhance quality. FMECA comprises two main components: Failure Mode Effect Analysis (FMEA) and Risk Analysis (RA). Within FMEA, the system is scrutinized to identify various failure modes that may occur, while also investigating the effects and causes associated with each failure. Risk analysis, on the other hand, entails evaluating the criticality of each potential failure mode to gauge the severity of its impact. FMEA of dumper To facilitate the analysis, the complete dumper system is divided into eight distinct sub-systems or components, namely brake, engine, steering, radiator, suspension transmission, hydraulic, and turbocharger as shown in Fig. 1 . For each component, failure modes have been identified and their causes and effects have been thoroughly examined using a fishbone diagram. The prepared fishbone diagrams are illustrated in the figures form Fig. 2 to 9 . Table 1 presents the FMEA table for the eight dumper components. Table 1 FMEA of Dumper components Component Function Failure modes Failure effect description Failure Cause(s) Brake Impede motion by absorbing energy F 11 - Brake oil leak Loss of Brake pressure Oil seal failure F 12 - Brake Jam Motion can’t be stopped Development of hotspots F 13 - Brake wear Improper function of brakes Worn-out brake pads Suspension Reduces the abrupt impact and excessive bouncing F 21 - Suspension bolt broken Entire vehicle may vibrate Excessive bouncing F 22 - Suspension Oil Leak Motion may become bouncy Oil seal failure F 23 - Suspension seals fail Minimizes vehicle stability Overload Engine To convert thermal/ chemical energy into mechanical energy F 31 - Engine replaced Loss of production Contamination of fuel F 32 - Engine head failed Coolant and oil leak Rust/scale formation F 33 - Exhaust bolt broken Noisy operation Defective material Transmission Transfer of power to the driveshaft and rear wheels or front wheels or both F 41 - Transmission oil leaked Delayed gear engagement Worn out gears F 42 - Clutch fail Loss of power efficiency Wear of parts F 43 - Torque convertor failed Shuddering of system Improper lubrication and worn bearing Steering Conversion of rotary movement into angular turns of front wheels F 51 - Steering ball bearing broken Poor turning tendency Steering drift F 52 - Toe in toe out Minimized turning tendency Bumpy roads F 53 - Steering pump failed Stoppage of fluid supply Overheating F 54 - Steering box failed Risk in driving Overloading Hydraulic To raise or lower heavy load F 61 - Hydraulic oil leak Inefficient operation Seal damage F 62 - Hoist cylinder oil leak Reduction in raising efficiency Loose assembly F 63 - Hoist cylinder bolt broken Loads can’t be raised Overloading Radiator To provide cooling effect to the engine by dissipating the heat F 71 - Water boil in radiator Increased water pressure Thermostat malfunction F 72 - Water pump leak Improper circulation Damaged water pump Turbocharger Boosts the air entering into the engine cylinder F 81 - Turbocharger failed Slow acceleration and Loss of power Poor maintenance Risk analysis of dumper using traditional FMECA RPN serves as a metric for prioritizing failure modes within a system and determining preventive measures to mitigate risks and minimize failure incidents. Additionally, this metric aids in establishing control mechanisms for effective failure detection. The risk priority of failure modes is evaluated by considering three key indices: Occurrence (O), Severity (S), and Detection (D). To calculate the RPN, two sequential steps are followed, which are outlined as follows. Step − 1 : Establishing O, S and D scales By taking into account the failure data of the dumper, the criteria for evaluating Occurrence (O), Severity (S), and Detection (D) were methodically constructed. The following is a more detailed explanation of the O, S, and D criterion scales: i. Occurrence (O) Occurrence refers to the frequency of failure events. To assess occurrence, a rating scale ranging from 1 to 10 was devised based on the number of observed failures. The proposed occurrence rating scale, together with the related ranks and ranges of failure frequencies, are shown in Table 2 . A rating of 10 signifies a high likelihood of occurrence, while a rating of 1 suggests a very low probability of occurrence. ii. Severity (S) In this study, the severity of failure is evaluated based on the impact it has on the availability of the system in the field. The severity assessment considers how significantly the consequences of a failure affect the service's availability. A statistic known as Time To Repair (TTR) can be used to quantify how each failure mode affects service availability. The severity is determined based on the TTR value, where a higher TTR value indicates a higher severity. The rankings of severity, along with their corresponding TTR values, are presented in Table 2 . The severity scale follows a 10-point range, where a rating of 10 denotes a serious impact resulting in greater unavailability, while a rating of 1 indicates a non-serious impact with good availability. iii. Detection (D) Detection evaluates the likelihood of detecting or identifying a failure before it occurs. By evaluating the data in Table 2 , it is possible to estimate the detectability of failure modes.This assessment takes into account various factors related to the ability to detect failures. Table 2 Occurrence ranking criteria Occurrence level Severity effect Detection control Rank Once in 24 months Interruption is ≤ 5 hours Almost certain to detect 1 Once in 18 months Interruption is > 5 hours and ≤ 10 hours Very high chance of detection 2 Once in 12 months Interruption is > 10 hours and ≤ 15 hours High chance of detection 3 Once in 9 months Interruption is > 15 hours and ≤ 25 hours Moderately high chance of detection 4 Once in 7 months Interruption is > 25 hours and ≤ 35 hours Medium chance of detection 5 Once in 6 months Interruption is > 35 hours and ≤ 45 hours Low chance of detection 6 Once in 5 months Interruption is > 45 hours and ≤ 55 hours Slight chance of detection 7 Once in 4 months Interruption is > 55 hours and ≤ 65 hours Remote chance of detection 8 Once in 3 months Interruption is > 65 hours and ≤ 75 hours Very remote chance of detection 9 Once in 2 months Interruption is > 75 hours No chance of detection 10 Step − 2 : Calculating RPN and assigning rank The RPN (Risk Priority Number) is calculated by multiplying the assigned scales for Occurrence (O), Severity (S), and Detection (D). Ranks are then allocated based on the resulting RPN values for each failure mode. Table 3 presents the scores of O, S, and D for each failure mode, along with the corresponding RPN values. A higher RPN value indicates a greater level of importance or criticality for the respective failure mode in the load transport system. Given that the rating values for Occurrence (O), Severity (S), and Detection (D) range from 1 to 10, the resulting RPN values will fall within the range of 1 to 1000. The ranks are then assigned to all the failure modes and prioritized in descending order. The highest-ranked failure mode represents the one that occurs most frequently and leads to significant production loss, without being predictable until it actually happens. This failure mode poses the greatest risk within the system. Risk analysis using RPN reveals that failure modes (F53, F63), (F13, F81), (F11, F32, F62, F71), (F41, F51, F61) have identical RPN values and hence have same rank. All of these modes, meanwhile, may not have the same effects on the dumper. Therefore, it is illogical to have the same RPN and rank for multiple failure modes. In addition, the findings show that failure of the steering box has the lowest risk and an exhaust bolt damaged has the highest risk. Table 3 RPN values for failure modes Failure Mode O S D RPN Rank Failure Mode O S D RPN Rank F 11 8 5 3 120 13 F 43 5 10 8 400 2 F 12 7 4 5 140 11 F 51 4 3 9 108 14 F 13 6 7 5 210 5 F 52 8 4 6 192 6 F 21 4 5 9 180 7 F 53 4 9 6 216 4 F 22 10 5 3 150 9 F 54 5 3 7 105 15 F 23 8 4 5 160 8 F 61 9 4 3 108 14 F 31 5 10 7 350 3 F 62 10 4 3 120 13 F 32 3 5 8 120 13 F 63 4 6 9 216 4 F 33 6 10 9 540 1 F 71 8 5 3 120 13 F 41 9 4 3 108 14 F 72 7 7 3 147 10 F 42 8 4 4 128 12 F 81 6 5 7 210 5 Fuzzy FMECA In comparison to traditional FMECA, fuzzy FMECA provides a better method for addressing ideas with limited knowledge and uncertainty. Similar to classic FMECA, fuzzy FMECA contains Occurrence (O), Severity (S), and Detection (D) as input variables. The Risk Priority Number (RPN) is the analysis's output variable. Instead of using crisp input values for O, S, and D rating scales, fuzzy FMECA employs non-crisp fuzzy values derived from linguistic terms through membership functions. These linguistic input variables are then fed into the fuzzy engine. Membership functions can serve as interpretive models to determine the linguistic terms. The Fuzzy RPN (F-RPN) can be generated by fuzzing the O, S, and D failure mode values. This strategy serves as the cornerstone for getting the fuzzy RPN. The degree of membership for representing the output variable was established using a risk rating scale developed utilizing the crisp RPN values. Additionally, qualitative phrases are also used to express these RPN values. Fuzzy rules can be created based on qualitative claims once the degree of membership for both input and output variables has been established. The "if-then" type rules are constructed based on the linguistic classes of O, S, and D and their related RPN classes in order to formulate the rules. These linguistically hazy variables are used as the input for the defuzzification stage, which results in a precise numerical value. In fuzzy FMECA, this precise value is known as the fuzzy RPN (F-RPN). Defuzzification can be carried out using various techniques, with the centroid method being the most commonly utilized. The centroid method calculates the central point of the area under the fuzzy set, resulting in a crisp value. As a result, the fuzzy inference system, which follows a rule-based methodology, produces a fuzzy RPN as its output. Figure 3 depicts the overall process for doing Fuzzy FMECA. Risk analysis of dumper using Fuzzy FMECA Fuzzy FMECA is implemented using the Fuzzy Logic Toolbox of MATLAB, utilizing three input variables and one output variable. The Fuzzy Logic Toolbox offers two methods: Mamdani and Sugeno. In the present work, the Mamdani method of inference is employed, with the center of gravity (centroid) as the defuzzification method. The execution of the fuzzy FMECA is conducted through three stages, which are elaborated below. Fuzzification During the process of fuzzification, the crisp values are converted into fuzzy values using membership functions. The crisp data for O, S, and D is represented as a fuzzy model using qualitative terms and corresponding membership functions. Tables 4 , 5 , and 6 provide an overview of the classification criteria for O, S, and D, expressed in linguistic terms. Each input variable is divided into ten levels, resulting in a total of 1000 combinations for the fuzzy rule base. In this work, Gaussian-type membership functions are utilized due to the different degrees of the input and output variables. Figures 11 (a), 11(b), and 11(c) illustrate the graphical representation of occurrence, severity, and detection membership functions in the fuzzy model. These visualizations serve as the foundation for generating non-crisp input variables in the fuzzy Failure Mode, Effects, and Criticality Analysis (FMECA). Similarly, the degree of membership for the output variable (RPN) is also determined by employing a Gaussian-type approach to transform RPN values into fuzzy RPN. To facilitate this conversion, the output RPN values are grouped into ten interval classes, as presented in Table 7 . The fuzzy representation of the output variable RPN is depicted in Fig. 12 , showcasing the membership function. This representation is utilized to convert the qualitative term into a numerical value through the process of defuzzification. Table 4 Fuzzy linguistic terms for occurrence Occurrence level Linguistic term Fuzzy number Occurrence level Linguistic term Fuzzy number Once in 24 months Unlikely (U) (0.43, 1) Once in 6 months Moderate (M) (0.43, 6) Once in 18 months Very remote (VR) (0.43, 2) Once in 5 months Moderately high (MH) (0.83, 7.5) Once in 12 months Remote (R) (0.43, 3) Once in 4 months High (H) (0.86, 9.5) Once in 9 months Very low (VL) (0.43, 4) Once in 3 months Very high (VH) (1.66, 12.5) Once in 7 months Low (L) (0.43, 5) Once in 2 months Almost certain (AC) (1.73, 16.5) Table 5 Fuzzy linguistic terms for severity Severity effect Linguistic term Fuzzy number Severity effect Linguistic term Fuzzy number Int. is ≤ 5 hrs. N (2.5, 2.5) Int. is > 35 hrs.&≤ 45 hrs. S (4.3, 40.5) Int. is > 5 hrs.& ≤ 10 hrs. VM (2.15, 8) Int. is > 45 hrs.&≤ 55 hrs. MA (4.3, 50.5) Int. is > 10 hrs.& ≤ 15 hrs. MI (2.15, 13) Int. is > 55 hrs.&≤ 65 hrs. E (4.3, 60.5) Int. is > 15 hrs.& ≤ 25 hrs. L (4.3, 20.5) Int. is > 65 hrs.&≤ 75 hrs. VE (4.3, 70.5) Int. is > 25 hrs.&≤ 35 hrs. MD (4.3, 30.5) Int. is > 75 hrs. S (10.2,87.5) Int. – Interruption, Hrs. – Hours, N-None, VM-Very Minor, MI-Minor, L-Low, MD-Moderate, S-Significant, MA-Major, E-Extreme, VE-Very Extreme, S-Serious Table 6 Fuzzy linguistic terms for detection Detection control Linguistic term Fuzzy number Detection control Linguistic term Fuzzy number Almost certain to detect AC (0.43, 1) Low chance of detection L (0.43, 6) Very high chance of detection VH (0.43, 2) Slight chance of detection VL (0.43, 7) High chance of detection H (0.43, 3) Remote chance of detection R (0.43, 8) Moderately high chance of detection MH (0.43, 4) Very remote chance of detection VR (0.43, 9) Medium chance of detection M (0.43, 5) No chance of detection AU (0.43, 10) AC-Almost Certain, VH-Very High, H-High, MH-Moderate High, M-Moderate, L-Low, VL-Very Low, R-Remote, VR-Very Remote, AU-Almost uncertain Table 7 Fuzzy linguistic terms for RPN RPN range Linguistic term Fuzzy number RPN range Linguistic term Fuzzy number RPN ≤ 10 N (4.5, 5) 120 < RPN ≤ 160 M (17, 140.5) 10 < RPN ≤ 25 VL (6, 18) 160 < RPN ≤ 250 HM (39, 205.5) 25 < RPN ≤ 50 L (11, 38) 250 < RPN ≤ 400 LH (63, 325.5) 50 < RPN ≤ 80 HL (12.14, 65.5) 400 < RPN ≤ 550 H (64, 475.5) 80 < RPN ≤ 120 LM (17. 100.5) 550 < RPN ≤ 1000 VH (189, 775.5) N-None, VL-Very Low, L-Low, HL-High Low, LM-Low Medium, M-Medium, HM-High Medium, LH-Low Medium, H-High, VH-Very High Establishing Fuzzy rules The formulation of rules constitutes a significant aspect of the FIS (Fuzzy Inference System) model. These rules are typically expressed in natural language, utilizing qualitative terms and adopting if-then structures. Their purpose is to establish the relationship between input and output membership functions. In this study, a total of 1000 if-then rules (10 for occurrence, 10 for severity, and 10 for detection) were established for the analysis. These rules encompass all possible combinations of O, S, and D. Table 8 presents a selection of linguistic combinations of O, S, and D, along with the corresponding linguistic terms for RPN, which serve as the basis for rule formulation. A subset of developed rules is displayed in Table 9 . These established rules have been integrated into the rule viewer tab of MATLAB, as depicted in Fig. 13 . Table 8 Development of inference rules (Refer Table 4 to 7 for notations used in this table) Severity: N Occurrence Fuzzy RPN U VR R VL L M MH H VH AC Detection AC N N N N N N N N N N VH N N N N N VL VL VL VL VL H N N N VL VL VL VL VL L L MH N N VL VL VL VL L L L L M N N VL VL VL L L L L L L N VL VL VL L L L L HL HL VL N VL VL L L L L HL HL HL R N VL VL L L L HL HL HL HL VR N VL L L L HL HL HL LM LM AU N VL L L L HL HL HL LM LM Table 9 Sample established rules INPUT OUTPUT Rule No. IF THEN Severity Occurrence Detection Risk 1 None Unlikely Almost certain None 2 None Unlikely Very high None 3 None Unlikely High None 4 None Unlikely Moderate high None 5 None Unlikely Moderate None 6 None Unlikely Low None : : : : : : : : : : : : : : : 240 Minor Very Low Almost uncertain Low medium 241 Minor Low Almost certain Very low 242 Minor Low Very high Low 243 Minor Low High Low 244 Minor Low Moderate high High low : : : : : : : : : : : : : : : 999 Serious Almost certain Very remote Very high 1000 Serious Almost certain Almost uncertain Very high Defuzzification RPN, a qualitative output variable produced by the FIS knowledge base, needs to be defuzzed in order to yield F-RPN in the form of crisp values. The defuzzification interface module receives the fuzzy linguistic variable as an input and transforms the output into sharp values that represent the fuzzy RPN. These precise values represent the failure modes' order of importance. To translate language rules into numerical outcomes, the Mamdani fuzzy inference model is used. The centre point of the region covered by the fuzzy set is identified using the centroid method, which is also employed as a defuzzification procedure, producing a crisp value. The rule viewer, shown in Fig. 14 , shows the precise value of RPN. It grows more complex by changing the input values for O, S, and D. the results of RPN and F-RPN are shown in Table 10 . Table 10 Traditional RPN and Fuzzy RPN values Failure Mode FRPN Rank RPN Rank Failure Mode FRPN Rank RPN Rank F 11 136 15 120 13 F 43 358 2 400 2 F 12 163 14 140 11 F 51 127 19 108 14 F 13 258 5 210 5 F 52 224 7 192 6 F 21 195 11 180 7 F 53 227 6 216 4 F 22 164 13 150 9 F 54 124 20 105 15 F 23 216 9 160 8 F 61 96.1 21 108 14 F 31 347 3 350 3 F 62 129 17 120 13 F 32 128 18 120 13 F 63 223 8 216 4 F 33 575 1 540 1 F 71 131 16 120 13 F 41 94.1 22 108 14 F 72 208 10 147 10 F 42 173 12 128 12 F 81 269 4 210 5 Conclusion This paper focuses on performing failure mode analysis using FMECA (Failure Mode, Effects, and Criticality Analysis). The aim is to utilize risk analysis as a means to prioritize failure modes and recommend necessary maintenance actions. However, traditional FMECA has been observed to have certain drawbacks, such as the occurrence of identical RPNs, lack of a scientific foundation, and wide variations in RPN values. To overcome these limitations, the application of fuzzy logic presents significant advantages. The approach of fuzzy FMECA combines FMECA with fuzzy logic using the MATLAB fuzzy logic toolbox. By employing linguistic terms and rules, Fuzzy RPN values are obtained based on three attributes. The results obtained through the fuzzy FMECA approach offer a comprehensive understanding of failure behaviour or tendencies. Furthermore, the issue of obtaining identical RPN values in traditional FMECA for different O, S, and D values is effectively addressed. FMECA using fuzzy logic reveals distinct rankings for each failure mode based on defuzzified RPN values determined by the fuzzy model. Declarations Compliance with Ethical Standards Authors have no potential conflicts of interest. The manuscript has not been submitted to more than one journal for simultaneous consideration. The manuscript has not been published previously. The Research not involved human participants and/or animals. 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Int J Syst Assur Eng Manag 11(2):402–411 Dejan V, Petrovi´c, Miloš Tanasijevi´c, Stojadinovi´c Saša, Jelena Ivaz and, Stojkovi P (2020) Fuzzy Model for Risk Assessment of Machinery Failures, Symmetry, vol. 12, p. 525 Cite Share Download PDF Status: Published Journal Publication published 16 Sep, 2024 Read the published version in International Journal of System Assurance Engineering and Management → Version 1 posted Editorial decision: Major revisions 24 Jun, 2024 Reviewers agreed at journal 21 May, 2024 Reviewers invited by journal 21 May, 2024 Editor invited by journal 28 Mar, 2024 First submitted to journal 27 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4179461","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305134326,"identity":"18b5183f-65dc-4942-a0f4-9b8119918260","order_by":0,"name":"M Pradeep Kumar","email":"","orcid":"","institution":"Kamala Institute of Technology and Science","correspondingAuthor":false,"prefix":"","firstName":"M","middleName":"Pradeep","lastName":"Kumar","suffix":""},{"id":305134327,"identity":"280ea76f-051f-4cae-b913-85a8a7a3c260","order_by":1,"name":"N.V.S. Raju","email":"","orcid":"","institution":"JNTUH College of Engineering: Jawaharlal Nehru Technological University Hyderabad College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"N.V.S.","middleName":"","lastName":"Raju","suffix":""},{"id":305134328,"identity":"40c1bcdc-f9d3-4638-a1f2-ce952509a72d","order_by":2,"name":"M.V. Satish Kumar","email":"","orcid":"","institution":"Kamala Institute of Technology and Science","correspondingAuthor":false,"prefix":"","firstName":"M.V.","middleName":"Satish","lastName":"Kumar","suffix":""},{"id":305134329,"identity":"43abfa99-7a1c-40ac-9f36-24c8e4af7169","order_by":3,"name":"GAJANAND GUPTA","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-9028-3491","institution":"VIT University","correspondingAuthor":true,"prefix":"","firstName":"GAJANAND","middleName":"","lastName":"GUPTA","suffix":""}],"badges":[],"createdAt":"2024-03-28 04:25:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4179461/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4179461/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13198-024-02506-5","type":"published","date":"2024-09-16T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57874845,"identity":"27ef4506-c0dc-415c-b3b6-d0a84b27b850","added_by":"auto","created_at":"2024-06-06 18:48:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21997,"visible":true,"origin":"","legend":"\u003cp\u003eDumper components\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/f26616d81b5edd8b27522c4b.png"},{"id":57873594,"identity":"d9baa4a0-ee5e-41d2-acf7-3d3b86baebf9","added_by":"auto","created_at":"2024-06-06 18:40:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":110561,"visible":true,"origin":"","legend":"\u003cp\u003eFishbone diagram for brake failure\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/545125b62b263db3da3ae720.png"},{"id":57873596,"identity":"167a081a-66f4-47a8-8737-4ae97c8a94ac","added_by":"auto","created_at":"2024-06-06 18:40:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":138521,"visible":true,"origin":"","legend":"\u003cp\u003eFishbone diagram for suspension failure\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/120ab23f4712eadda2111f8a.png"},{"id":57873590,"identity":"aba78aa3-b9fb-45d6-8cbb-5b9f91c4cd77","added_by":"auto","created_at":"2024-06-06 18:40:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":110166,"visible":true,"origin":"","legend":"\u003cp\u003eFishbone diagram for engine failure\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/a782ed1794914c06f989a3e5.png"},{"id":57873595,"identity":"2898bd5c-1936-4bcc-9869-cf7ca5dd5615","added_by":"auto","created_at":"2024-06-06 18:40:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":127596,"visible":true,"origin":"","legend":"\u003cp\u003eFishbone diagram for transmission failure\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/126aaabe72f2a9bcfd6db070.png"},{"id":57873588,"identity":"9a86ab83-ec05-49d0-b783-b6dbb7cf7b22","added_by":"auto","created_at":"2024-06-06 18:40:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":149684,"visible":true,"origin":"","legend":"\u003cp\u003eFishbone diagram for steering failure\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/9d9c23c6ecf30f50d78b46e0.png"},{"id":57873591,"identity":"8d7bbbae-8438-40f6-bbdc-077fd6705378","added_by":"auto","created_at":"2024-06-06 18:40:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":119356,"visible":true,"origin":"","legend":"\u003cp\u003eFishbone diagram for hydraulic failure\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/60d93b241d2c94c2a1527470.png"},{"id":57873598,"identity":"2c3a6ede-c5bf-45f8-a0b0-61f454424616","added_by":"auto","created_at":"2024-06-06 18:40:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":122335,"visible":true,"origin":"","legend":"\u003cp\u003eFishbone diagram for radiator failure\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/cf1f1f660565a49d9d63718f.png"},{"id":57873601,"identity":"246498b7-5160-4337-9cb4-0fc82ae9c1bc","added_by":"auto","created_at":"2024-06-06 18:40:44","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":86391,"visible":true,"origin":"","legend":"\u003cp\u003eFishbone diagram for turbocharger failure\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/496b54cbb8d36fa01b37288a.png"},{"id":57873599,"identity":"bedadc39-e870-4dc9-8358-353ff5a43715","added_by":"auto","created_at":"2024-06-06 18:40:43","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":51797,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of Fuzzy FMECA\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/9b33b9f2aa8ad13aa5c86e75.png"},{"id":57873592,"identity":"e0c56558-fabf-4a25-a933-6271411ca02d","added_by":"auto","created_at":"2024-06-06 18:40:43","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":151319,"visible":true,"origin":"","legend":"\u003cp\u003e(a): Membership function of occurrence\u003c/p\u003e\n\u003cp\u003e(b): Membership function of severity\u003c/p\u003e\n\u003cp\u003e(c): Membership function of detection\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/5c26dfda0679f088cd421a14.png"},{"id":57873600,"identity":"dec0a84a-8f3f-4156-ba7c-77076a0ef003","added_by":"auto","created_at":"2024-06-06 18:40:43","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":75707,"visible":true,"origin":"","legend":"\u003cp\u003eMembership Function of RPN\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/2c0d98a9674078690bafa4ec.png"},{"id":57874846,"identity":"1bc9983e-9a59-45ac-b580-9b2d4ac3780b","added_by":"auto","created_at":"2024-06-06 18:48:44","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":27480,"visible":true,"origin":"","legend":"\u003cp\u003e1000 rules in Rule editor of MATLAB\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/70f0dfe8061c8521dc14f378.png"},{"id":57873589,"identity":"4d6558c0-45f3-47be-afff-acbd0f42a67c","added_by":"auto","created_at":"2024-06-06 18:40:42","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":20706,"visible":true,"origin":"","legend":"\u003cp\u003eRule viewer in MATLAB\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/bb387c3ec2061cd104ff99d6.png"},{"id":65104200,"identity":"3eab0c63-4442-451a-8d30-c35ac1d973bf","added_by":"auto","created_at":"2024-09-23 16:12:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2037583,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4179461/v1/88a31e5d-37e9-47d5-9ba6-ddf935edd895.pdf"}],"financialInterests":"","formattedTitle":"Risk Assessment and Prioritization using Fuzzy FMECA: A Case Study of Dumper Breakdowns","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoal mining is the process of extracting coal from the earth\u0026rsquo;s surface and also from the underground [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Open pit mining is a way where coal is extracted by removing a layer of soil and rock bed [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Different types of machinery and equipment are used in open pit mining for the removal and transport of coal. Bull dozer, dragline, bucket wheel excavator, drill rig, dumpers are some of the equipment used in open pit mining [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In order to meet established production targets, mining organizations require more reliable equipment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Reliability specifies the overall machine condition and it can be defined as the probabilitythat a device or a system will perform its function satisfactorily for a given time periodwhen used according to stated operating conditions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Around 12%-15% of manufacturing time iswasted by unplanned maintenance in open pit mining [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and also the cost of maintenance is morethan 60% of the operation cost [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, the reliability of mining equipment is a major problem that needs to be addressed.\u003c/p\u003e \u003cp\u003eFailure Mode Effect and Criticality Analysis (FMECA) is a sophisticated technique used in industries to predict and enhance the reliability of equipment. It identifies and prioritizes the failure points of a system/sub-system before they occur. Thisconcept was originated with the US Army in 1949 to improve and inspect the reliability of military systems [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. FMECA is proved to be one of the prominent preventive action methodologies which can avoid sudden failures. FMECA uses Risk Priority Number (RPN) which is a measure of the overall risk of a failure mode. RPN is calculated by multiplying three indices i.e., Occurrence (O), Severity (S) and Detection (D) as given in Eq.\u0026nbsp;1. These three factors are evaluated for each failure mode on a ten-point scale, usually from 10 to 1.\u003c/p\u003e \u003cp\u003eRisk Priority Number =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({(\\text{O}}_{\\text{i}\\text{j}})\\times {(\\text{S}}_{\\text{i}\\text{j}})\\times {(\\text{D}}_{\\text{i}\\text{j}})\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003e(i\u0026thinsp;=\u0026thinsp;1,2,3\u0026hellip;.n \u0026amp; j\u0026thinsp;=\u0026thinsp;1,2,3\u0026hellip;.m)\u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({O}_{ij}{, S}_{ij} and {D}_{ij}\\)\u003c/span\u003e\u003c/span\u003e refers to the occurrence, severity and detection of i\u003csup\u003eth\u003c/sup\u003e failure mode of j\u003csup\u003eth\u003c/sup\u003e component.\u003c/p\u003e \u003cp\u003eFailure modes with the greatest RPN values are thought to be more significant and should be resolved before those with lower RPN values.\u003c/p\u003e \u003cp\u003eOne of the effective risk assessment methods that examines probable failures in order to prevent them is traditional FMECA. However, conducting classic FMECA only has a few drawbacks. The list of these negatives is shown below.\u003c/p\u003e \u003cp\u003ea. Duplicate RPNs\u003c/p\u003e\u003cp\u003eWhile different O, S, and D rank combinations may provide exactly the same RPN number, their hidden risk implications may vary. RPN values 270, 210, and 168, for instance, can be found in twelve different O, S, and D combinations. Similar rank and RPN values for several clusters may not necessarily reflect the same level of danger. Wasted time and resources may result from this, and in some situations, high-risk failure modes might go undiscovered.\u003c/p\u003e \u003cp\u003eb. Gaps in the range\u003c/p\u003e \u003cp\u003eThe majority of values between 1 and 1000 cannot be formed, hence the RPN values produced by multiplying O, S, and D are not continuous. Only 120 out of 1,000 are RPN numbers, meaning that 88% of the range is unfilled. Therefore, RPN cannot display any integers that are multiples of 11. The multiples of 13, 17, and 19 are likewise disregarded. Following 1000, 900 is the biggest RPN value, followed by 810 and 800.\u003c/p\u003e \u003cp\u003ec. Lacks scientific base\u003c/p\u003e\u003cp\u003eThe mathematical computation for obtaining RPN is debated as it lacks a complete scientific basis. There is no justification for multiplying the O, S, and D parameters to produce the RPN.\u003c/p\u003e \u003cp\u003ed. Uncertain information\u003c/p\u003e\u003cp\u003eIt is challenging to assess the three risk variables O, S, and D properly. The majority of the data in FMECA is frequently ambiguous or questionable. For accurate assessment, language phrases like \"medium,\" \"many,\" or \"very high\" might be employed.\u003c/p\u003e \u003cp\u003eApplying fuzzy logic can be highly advantageous in addressing the limitations of conventional FMECA methods. The inherent uncertainty involved in the decision-making process may be successfully described by using fuzzy theories. This method helps to eliminate the ambiguity and uncertainty seen in conventional FMECA. Fuzzy logic was suggested by Bowles J. B. et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] as a method for rating the failure scenarios in FMECA. Based on the RPNs acquired through FMECA, Feliks and Bukowski [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] devised a fuzzy logic approach to evaluate the risk in a system. The fuzzy logic-based FMECA methodology may be streamlined by Lim and Tay [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] by reducing the number of rules needed to achieve F-RPN. Chin et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] suggested employing fuzzy FMEA to assess the idea of product development. Using a fuzzy logic model, Liu and Tsai [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] created preventative strategies for workplace risks in the construction sector. Albeanu G. et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] developed a methodology using intuitionistic-fuzzy numbers to assess the reliability of wind turbine. Gajanand. G et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] proposed a study on milling machine using fuzzy logic for reliability-centered maintenance. Renjith V. R. et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] employed the Fuzzy FMECA approach to address the issues in failure mode prioritization of LNG storage facility. Gajanand G and Mishra R.P. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] analysed the criticality of lathe machine using traditional and fuzzy approaches of FMECA. Dejan V. P. et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] implemented a methodology by interlinking fuzzy sets and statistical methods to analyze the risk level of a system.\u003c/p\u003e \u003cp\u003eThe present study extends the application of fuzzy logic to FMECA, specifically focusing on prioritizing failure modes. Fuzzy FMECA offers a superior approach that can produce reliable results, even when faced with limited information, in contrast to traditional FMECA.\u003c/p\u003e"},{"header":"Traditional FMECA","content":"\u003cp\u003eThe conventional FMECA serves as a means to assess possible failure modes and their underlying causes. Its primary purpose is to prioritize these failure modes and provide recommendations for preventive measures, aiming to avert catastrophic failures and enhance quality. FMECA comprises two main components: Failure Mode Effect Analysis (FMEA) and Risk Analysis (RA). Within FMEA, the system is scrutinized to identify various failure modes that may occur, while also investigating the effects and causes associated with each failure. Risk analysis, on the other hand, entails evaluating the criticality of each potential failure mode to gauge the severity of its impact.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFMEA of dumper\u003c/h2\u003e \u003cp\u003eTo facilitate the analysis, the complete dumper system is divided into eight distinct sub-systems or components, namely brake, engine, steering, radiator, suspension transmission, hydraulic, and turbocharger as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For each component, failure modes have been identified and their causes and effects have been thoroughly examined using a fishbone diagram. The prepared fishbone diagrams are illustrated in the figures form Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e to \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the FMEA table for the eight dumper components.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFMEA of Dumper components\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFailure modes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFailure effect description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFailure Cause(s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBrake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eImpede motion by absorbing energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e11\u003c/sub\u003e - Brake oil leak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoss of Brake pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil seal failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e12\u003c/sub\u003e - Brake Jam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMotion can\u0026rsquo;t be stopped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDevelopment of hotspots\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e13\u003c/sub\u003e - Brake wear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImproper function of brakes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorn-out brake pads\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSuspension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eReduces the abrupt impact and excessive bouncing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e21\u003c/sub\u003e - Suspension bolt broken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEntire vehicle may vibrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcessive bouncing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e22\u003c/sub\u003e - Suspension Oil Leak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMotion may become bouncy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil seal failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e23\u003c/sub\u003e - Suspension seals fail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimizes vehicle stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverload\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEngine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTo convert thermal/ chemical energy into mechanical energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e31\u003c/sub\u003e - Engine replaced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoss of production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eContamination of fuel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e32\u003c/sub\u003e - Engine head failed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoolant and oil leak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRust/scale formation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e33\u003c/sub\u003e - Exhaust bolt broken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNoisy operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDefective material\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTransmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTransfer of power to the driveshaft and rear wheels or front wheels or both\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e41\u003c/sub\u003e - Transmission oil leaked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDelayed gear engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorn out gears\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e42\u003c/sub\u003e - Clutch fail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoss of power efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWear of parts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e43\u003c/sub\u003e - Torque convertor failed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShuddering of system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImproper lubrication and worn bearing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSteering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eConversion of rotary movement into angular turns of front wheels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e51\u003c/sub\u003e - Steering ball bearing broken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePoor turning tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSteering drift\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e52\u003c/sub\u003e - Toe in toe out\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimized turning tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBumpy roads\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e53\u003c/sub\u003e - Steering pump failed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStoppage of fluid supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverheating\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e54\u003c/sub\u003e - Steering box failed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRisk in driving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverloading\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHydraulic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTo raise or lower heavy\u0026nbsp;load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e61\u003c/sub\u003e - Hydraulic oil leak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInefficient operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeal damage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e62\u003c/sub\u003e - Hoist cylinder oil leak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReduction in raising efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLoose assembly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e63\u003c/sub\u003e - Hoist cylinder bolt broken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoads can\u0026rsquo;t be raised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverloading\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRadiator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTo provide cooling effect to the engine by dissipating the heat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e71\u003c/sub\u003e - Water boil in radiator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncreased water pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThermostat malfunction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e72\u003c/sub\u003e - Water pump leak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImproper circulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDamaged water pump\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbocharger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoosts the air entering into the engine cylinder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003e81\u003c/sub\u003e - Turbocharger failed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlow acceleration and Loss of power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePoor maintenance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRisk analysis of dumper using traditional FMECA\u003c/h2\u003e \u003cp\u003eRPN serves as a metric for prioritizing failure modes within a system and determining preventive measures to mitigate risks and minimize failure incidents. Additionally, this metric aids in establishing control mechanisms for effective failure detection. The risk priority of failure modes is evaluated by considering three key indices: Occurrence (O), Severity (S), and Detection (D). To calculate the RPN, two sequential steps are followed, which are outlined as follows.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep \u0026minus;\u0026thinsp;1\u003c/b\u003e: \u003cb\u003eEstablishing O, S and D scales\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBy taking into account the failure data of the dumper, the criteria for evaluating Occurrence (O), Severity (S), and Detection (D) were methodically constructed. The following is a more detailed explanation of the O, S, and D criterion scales:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ei. Occurrence (O)\u003c/h2\u003e \u003cp\u003eOccurrence refers to the frequency of failure events. To assess occurrence, a rating scale ranging from 1 to 10 was devised based on the number of observed failures. The proposed occurrence rating scale, together with the related ranks and ranges of failure frequencies, are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A rating of 10 signifies a high likelihood of occurrence, while a rating of 1 suggests a very low probability of occurrence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eii. Severity (S)\u003c/h2\u003e \u003cp\u003eIn this study, the severity of failure is evaluated based on the impact it has on the availability of the system in the field. The severity assessment considers how significantly the consequences of a failure affect the service's availability. A statistic known as Time To Repair (TTR) can be used to quantify how each failure mode affects service availability. The severity is determined based on the TTR value, where a higher TTR value indicates a higher severity. The rankings of severity, along with their corresponding TTR values, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The severity scale follows a 10-point range, where a rating of 10 denotes a serious impact resulting in greater unavailability, while a rating of 1 indicates a non-serious impact with good availability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eiii. Detection (D)\u003c/h2\u003e \u003cp\u003eDetection evaluates the likelihood of detecting or identifying a failure before it occurs. By evaluating the data in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it is possible to estimate the detectability of failure modes.This assessment takes into account various factors related to the ability to detect failures.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOccurrence ranking criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccurrence level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeverity effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetection control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterruption is \u0026le;\u0026thinsp;5 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlmost certain to detect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 18 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterruption is \u0026gt;\u0026thinsp;5 hours and \u0026le;\u0026thinsp;10 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery high chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterruption is \u0026gt;\u0026thinsp;10 hours and \u0026le;\u0026thinsp;15 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 9 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterruption is \u0026gt;\u0026thinsp;15 hours and \u0026le;\u0026thinsp;25 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerately high chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 7 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterruption is \u0026gt;\u0026thinsp;25 hours and \u0026le;\u0026thinsp;35 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterruption is \u0026gt;\u0026thinsp;35 hours and \u0026le;\u0026thinsp;45 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 5 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterruption is \u0026gt;\u0026thinsp;45 hours and \u0026le;\u0026thinsp;55 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlight chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 4 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterruption is \u0026gt;\u0026thinsp;55 hours and \u0026le;\u0026thinsp;65 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRemote chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 3 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterruption is \u0026gt;\u0026thinsp;65 hours and \u0026le;\u0026thinsp;75 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery remote chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 2 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterruption is \u0026gt;\u0026thinsp;75 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStep \u0026minus;\u0026thinsp;2\u003c/b\u003e: \u003cb\u003eCalculating RPN and assigning rank\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe RPN (Risk Priority Number) is calculated by multiplying the assigned scales for Occurrence (O), Severity (S), and Detection (D). Ranks are then allocated based on the resulting RPN values for each failure mode. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the scores of O, S, and D for each failure mode, along with the corresponding RPN values. A higher RPN value indicates a greater level of importance or criticality for the respective failure mode in the load transport system. Given that the rating values for Occurrence (O), Severity (S), and Detection (D) range from 1 to 10, the resulting RPN values will fall within the range of 1 to 1000. The ranks are then assigned to all the failure modes and prioritized in descending order. The highest-ranked failure mode represents the one that occurs most frequently and leads to significant production loss, without being predictable until it actually happens. This failure mode poses the greatest risk within the system. Risk analysis using RPN reveals that failure modes (F53, F63), (F13, F81), (F11, F32, F62, F71), (F41, F51, F61) have identical RPN values and hence have same rank. All of these modes, meanwhile, may not have the same effects on the dumper. Therefore, it is illogical to have the same RPN and rank for multiple failure modes. In addition, the findings show that failure of the steering box has the lowest risk and an exhaust bolt damaged has the highest risk.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRPN values for failure modes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFailure Mode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRPN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFailure Mode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRPN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e11\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003csub\u003e43\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e12\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003csub\u003e51\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e13\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003csub\u003e52\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e21\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003csub\u003e53\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e22\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003csub\u003e54\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e23\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003csub\u003e61\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e31\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003csub\u003e62\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e32\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003csub\u003e63\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e33\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003csub\u003e71\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e41\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003csub\u003e72\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e42\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003csub\u003e81\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Fuzzy FMECA","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cp\u003eIn comparison to traditional FMECA, fuzzy FMECA provides a better method for addressing ideas with limited knowledge and uncertainty. Similar to classic FMECA, fuzzy FMECA contains Occurrence (O), Severity (S), and Detection (D) as input variables. The Risk Priority Number (RPN) is the analysis's output variable. Instead of using crisp input values for O, S, and D rating scales, fuzzy FMECA employs non-crisp fuzzy values derived from linguistic terms through membership functions. These linguistic input variables are then fed into the fuzzy engine. Membership functions can serve as interpretive models to determine the linguistic terms. The Fuzzy RPN (F-RPN) can be generated by fuzzing the O, S, and D failure mode values. This strategy serves as the cornerstone for getting the fuzzy RPN.\u003c/p\u003e \u003cp\u003eThe degree of membership for representing the output variable was established using a risk rating scale developed utilizing the crisp RPN values. Additionally, qualitative phrases are also used to express these RPN values. Fuzzy rules can be created based on qualitative claims once the degree of membership for both input and output variables has been established. The \"if-then\" type rules are constructed based on the linguistic classes of O, S, and D and their related RPN classes in order to formulate the rules. These linguistically hazy variables are used as the input for the defuzzification stage, which results in a precise numerical value. In fuzzy FMECA, this precise value is known as the fuzzy RPN (F-RPN). Defuzzification can be carried out using various techniques, with the centroid method being the most commonly utilized. The centroid method calculates the central point of the area under the fuzzy set, resulting in a crisp value. As a result, the fuzzy inference system, which follows a rule-based methodology, produces a fuzzy RPN as its output. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depicts the overall process for doing Fuzzy FMECA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRisk analysis of dumper using Fuzzy FMECA\u003c/h2\u003e \u003cp\u003eFuzzy FMECA is implemented using the Fuzzy Logic Toolbox of MATLAB, utilizing three input variables and one output variable. The Fuzzy Logic Toolbox offers two methods: Mamdani and Sugeno. In the present work, the Mamdani method of inference is employed, with the center of gravity (centroid) as the defuzzification method. The execution of the fuzzy FMECA is conducted through three stages, which are elaborated below.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eFuzzification\u003c/h2\u003e\n\u003cp\u003eDuring the process of fuzzification, the crisp values are converted into fuzzy values using membership functions. The crisp data for O, S, and D is represented as a fuzzy model using qualitative terms and corresponding membership functions. Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e provide an overview of the classification criteria for O, S, and D, expressed in linguistic terms. Each input variable is divided into ten levels, resulting in a total of 1000 combinations for the fuzzy rule base. In this work, Gaussian-type membership functions are utilized due to the different degrees of the input and output variables. Figures\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e11\u003c/span\u003e(a), 11(b), and 11(c) illustrate the graphical representation of occurrence, severity, and detection membership functions in the fuzzy model. These visualizations serve as the foundation for generating non-crisp input variables in the fuzzy Failure Mode, Effects, and Criticality Analysis (FMECA). Similarly, the degree of membership for the output variable (RPN) is also determined by employing a Gaussian-type approach to transform RPN values into fuzzy RPN. To facilitate this conversion, the output RPN values are grouped into ten interval classes, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The fuzzy representation of the output variable RPN is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003e, showcasing the membership function. This representation is utilized to convert the qualitative term into a numerical value through the process of defuzzification.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFuzzy linguistic terms for occurrence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccurrence level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinguistic term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFuzzy number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOccurrence level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLinguistic term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFuzzy number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnlikely (U)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.43, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOnce in 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate (M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.43, 6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 18 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery remote (VR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.43, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOnce in 5 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerately high (MH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.83, 7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRemote (R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.43, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOnce in 4 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh (H)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.86, 9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 9 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery low (VL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.43, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOnce in 3 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVery high (VH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(1.66, 12.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce in 7 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.43, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOnce in 2 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlmost certain (AC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(1.73, 16.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFuzzy linguistic terms for severity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverity effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinguistic term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFuzzy number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeverity effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLinguistic term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFuzzy number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInt. is \u0026le;\u0026thinsp;5 hrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(2.5, 2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInt. is \u0026gt;\u0026thinsp;35 hrs.\u0026amp;\u0026le; 45 hrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(4.3, 40.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInt. is \u0026gt;\u0026thinsp;5 hrs.\u0026amp; \u0026le; 10 hrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(2.15, 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInt. is \u0026gt;\u0026thinsp;45 hrs.\u0026amp;\u0026le; 55 hrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(4.3, 50.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInt. is \u0026gt;\u0026thinsp;10 hrs.\u0026amp; \u0026le; 15 hrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(2.15, 13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInt. is \u0026gt;\u0026thinsp;55 hrs.\u0026amp;\u0026le; 65 hrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(4.3, 60.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInt. is \u0026gt;\u0026thinsp;15 hrs.\u0026amp; \u0026le; 25 hrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(4.3, 20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInt. is \u0026gt;\u0026thinsp;65 hrs.\u0026amp;\u0026le; 75 hrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(4.3, 70.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInt. is \u0026gt;\u0026thinsp;25 hrs.\u0026amp;\u0026le; 35 hrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(4.3, 30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInt. is \u0026gt;\u0026thinsp;75 hrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(10.2,87.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInt. \u0026ndash; Interruption, Hrs. \u0026ndash; Hours, N-None, VM-Very Minor, MI-Minor, L-Low, MD-Moderate, S-Significant, MA-Major, E-Extreme, VE-Very Extreme, S-Serious\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFuzzy linguistic terms for detection\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetection control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinguistic term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFuzzy number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDetection control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLinguistic term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFuzzy number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlmost certain to detect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.43, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.43, 6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery high chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.43, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlight chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.43, 7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.43, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRemote chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.43, 8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately high chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.43, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery remote chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.43, 9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.43, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo chance of detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.43, 10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAC-Almost Certain, VH-Very High, H-High, MH-Moderate High, M-Moderate, L-Low, VL-Very Low, R-Remote, VR-Very Remote, AU-Almost uncertain\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFuzzy linguistic terms for RPN\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRPN range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinguistic term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFuzzy number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRPN range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLinguistic term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFuzzy number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRPN\u0026thinsp;\u0026le;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.5, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u0026thinsp;\u0026lt;\u0026thinsp;RPN\u0026thinsp;\u0026le;\u0026thinsp;160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(17, 140.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026thinsp;\u0026lt;\u0026thinsp;RPN\u0026thinsp;\u0026le;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6, 18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160\u0026thinsp;\u0026lt;\u0026thinsp;RPN\u0026thinsp;\u0026le;\u0026thinsp;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(39, 205.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026thinsp;\u0026lt;\u0026thinsp;RPN\u0026thinsp;\u0026le;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(11, 38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250\u0026thinsp;\u0026lt;\u0026thinsp;RPN\u0026thinsp;\u0026le;\u0026thinsp;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(63, 325.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026thinsp;\u0026lt;\u0026thinsp;RPN\u0026thinsp;\u0026le;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(12.14, 65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400\u0026thinsp;\u0026lt;\u0026thinsp;RPN\u0026thinsp;\u0026le;\u0026thinsp;550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(64, 475.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026thinsp;\u0026lt;\u0026thinsp;RPN\u0026thinsp;\u0026le;\u0026thinsp;120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(17. 100.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e550\u0026thinsp;\u0026lt;\u0026thinsp;RPN\u0026thinsp;\u0026le;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(189, 775.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eN-None, VL-Very Low, L-Low, HL-High Low, LM-Low Medium, M-Medium, HM-High Medium, LH-Low Medium, H-High, VH-Very High\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEstablishing Fuzzy rules\u003c/h2\u003e \u003cp\u003eThe formulation of rules constitutes a significant aspect of the FIS (Fuzzy Inference System) model. These rules are typically expressed in natural language, utilizing qualitative terms and adopting if-then structures. Their purpose is to establish the relationship between input and output membership functions. In this study, a total of 1000 if-then rules (10 for occurrence, 10 for severity, and 10 for detection) were established for the analysis. These rules encompass all possible combinations of O, S, and D. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents a selection of linguistic combinations of O, S, and D, along with the corresponding linguistic terms for RPN, which serve as the basis for rule formulation. A subset of developed rules is displayed in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. These established rules have been integrated into the rule viewer tab of MATLAB, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDevelopment of inference rules (Refer Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e to \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e for notations used in this table)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSeverity: N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c12\" namest=\"c3\"\u003e \u003cp\u003eOccurrence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFuzzy RPN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eVH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003eDetection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eLM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eLM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample established rules\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eINPUT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOUTPUT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRule No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTHEN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSeverity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOccurrence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eDetection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRisk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnlikely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlmost certain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnlikely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnlikely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnlikely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnlikely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnlikely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlmost uncertain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow medium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlmost certain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVery low\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh low\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlmost certain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery remote\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlmost certain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlmost uncertain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDefuzzification\u003c/h2\u003e \u003cp\u003eRPN, a qualitative output variable produced by the FIS knowledge base, needs to be defuzzed in order to yield F-RPN in the form of crisp values. The defuzzification interface module receives the fuzzy linguistic variable as an input and transforms the output into sharp values that represent the fuzzy RPN. These precise values represent the failure modes' order of importance. To translate language rules into numerical outcomes, the Mamdani fuzzy inference model is used. The centre point of the region covered by the fuzzy set is identified using the centroid method, which is also employed as a defuzzification procedure, producing a crisp value. The rule viewer, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e14\u003c/span\u003e, shows the precise value of RPN. It grows more complex by changing the input values for O, S, and D. the results of RPN and F-RPN are shown in Table \u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTraditional RPN and Fuzzy RPN values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFailure Mode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFRPN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRPN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFailure Mode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFRPN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRPN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e11\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003csub\u003e43\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e12\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003csub\u003e51\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e13\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003csub\u003e52\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e21\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003csub\u003e53\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e22\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003csub\u003e54\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e23\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003csub\u003e61\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e 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\u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003csub\u003e81\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis paper focuses on performing failure mode analysis using FMECA (Failure Mode, Effects, and Criticality Analysis). The aim is to utilize risk analysis as a means to prioritize failure modes and recommend necessary maintenance actions. However, traditional FMECA has been observed to have certain drawbacks, such as the occurrence of identical RPNs, lack of a scientific foundation, and wide variations in RPN values. To overcome these limitations, the application of fuzzy logic presents significant advantages. The approach of fuzzy FMECA combines FMECA with fuzzy logic using the MATLAB fuzzy logic toolbox. By employing linguistic terms and rules, Fuzzy RPN values are obtained based on three attributes. The results obtained through the fuzzy FMECA approach offer a comprehensive understanding of failure behaviour or tendencies. Furthermore, the issue of obtaining identical RPN values in traditional FMECA for different O, S, and D values is effectively addressed. FMECA using fuzzy logic reveals distinct rankings for each failure mode based on defuzzified RPN values determined by the fuzzy model.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors have no potential conflicts of interest. The manuscript has not been submitted to more than one journal for simultaneous consideration. The manuscript has not been published previously. The Research not involved human participants and/or animals. Informed consent was obtained from all authors included in the study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eManna A, Maiti R (2014) Opencast Coal Mining Induced Defaced Topography of Raniganj Coalfield in India - Remote Sensing and GIS Based Analysis, J Indian Soc Remote Sens, vol. 42, no. 4, pp. 755\u0026ndash;764, Dec\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMudit Kulshreshtha, Jyoti KP (2002) Study of efficiency and productivity growth in opencast and underground coal mining in India: a DEA analysis, Energy Economics., Vol. 24, No. 5, pp. 439\u0026ndash;453, Sept\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBimal Samanta B, Sarkar, Mukherjee SK (2002) Selection of opencast mining equipment by a multi-criteria decision-making process. 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Quality and Reliability Engineering International, Wiley online library\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRenjith VR, Jose kalathil M, Haresh Kumar P, Madhavan D (2018) Fuzzy FMECA (Failure Mode Effect and Criticality Analysis) of LNG storage facility, Journal of Loss Prevention in the Process Industries, Vol. 56, pp. 537\u0026ndash;547, Nov\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGajanand Gupta, Mishra RP (July 2020) Comparative analysis of traditional and fuzzy FMECA approach for criticality analysis of conventional lathe machine. Int J Syst Assur Eng Manag 11(2):402\u0026ndash;411\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDejan V, Petrovi\u0026acute;c, Miloš Tanasijevi\u0026acute;c, Stojadinovi\u0026acute;c Saša, Jelena Ivaz and, Stojkovi P (2020) Fuzzy Model for Risk Assessment of Machinery Failures, Symmetry, vol. 12, p. 525\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-system-assurance-engineering-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijsa","sideBox":"Learn more about [International Journal of System Assurance Engineering and Management](http://link.springer.com/journal/13198)","snPcode":"13198","submissionUrl":"https://www.editorialmanager.com/ijsa/default2.aspx","title":"International Journal of System Assurance Engineering and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Risk Assessment, Dumper, FMECA, Fuzzy FMECA","lastPublishedDoi":"10.21203/rs.3.rs-4179461/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4179461/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe production and supply of coal are always prominent issues for thermal power plants due to the high demand for electricity generation. The dump trucks used in open-pit mining serve as a medium to transport the coal. Interrupted services of dump trucks due to breakdown can lead to delayed and incorrect supply to the generating units. To improve the productivity and for the proper delivery of the coal, the downtime should be minimized and the reliability should be maximized. FMECA is one of the prominent reliability modelling techniques that prevent occurrence of sudden failures. In order to overcome the shortcomings of traditional FMECA, fuzzy rule based approach is proposed in this paper to mitigate the failures in dumpers. Fuzzy FMECA helps in resolving the uncertainty and ambiguity in traditional FMECA approach and can work in a better way even using insufficient information when compared with traditional FMECA.\u003c/p\u003e","manuscriptTitle":"Risk Assessment and Prioritization using Fuzzy FMECA: A Case Study of Dumper Breakdowns","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 18:40:35","doi":"10.21203/rs.3.rs-4179461/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-06-24T11:56:38+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-05-21T17:07:51+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-21T16:37:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"International Journal of System Assurance Engineering and Management","date":"2024-03-28T07:24:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of System Assurance Engineering and Management","date":"2024-03-28T00:23:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-system-assurance-engineering-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijsa","sideBox":"Learn more about [International Journal of System Assurance Engineering and Management](http://link.springer.com/journal/13198)","snPcode":"13198","submissionUrl":"https://www.editorialmanager.com/ijsa/default2.aspx","title":"International Journal of System Assurance Engineering and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0c0782a9-6f54-4427-ab81-3aecec20a071","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-23T16:07:19+00:00","versionOfRecord":{"articleIdentity":"rs-4179461","link":"https://doi.org/10.1007/s13198-024-02506-5","journal":{"identity":"international-journal-of-system-assurance-engineering-and-management","isVorOnly":false,"title":"International Journal of System Assurance Engineering and Management"},"publishedOn":"2024-09-16 15:57:07","publishedOnDateReadable":"September 16th, 2024"},"versionCreatedAt":"2024-06-06 18:40:35","video":"","vorDoi":"10.1007/s13198-024-02506-5","vorDoiUrl":"https://doi.org/10.1007/s13198-024-02506-5","workflowStages":[]},"version":"v1","identity":"rs-4179461","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4179461","identity":"rs-4179461","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}