Improve  Reliability   Over Time And Repair and Replacement Rates With Two And Three Spare  Nodes Using The Duplex And Triplex Markov Model with AI and IoT In Industrial Wireless Sensor Networks

Improve Reliability Over Time And Repair and Replacement Rates With Two And Three Spare Nodes Using The Duplex And Triplex Markov Model with AI and IoT In Industrial Wireless Sensor Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Improve Reliability Over Time And Repair and Replacement Rates With Two And Three Spare Nodes Using The Duplex And Triplex Markov Model with AI and IoT In Industrial Wireless Sensor Networks Ahmad reza Zamani, Mohammad Ali pourmina, Ramin Shaghaghi Kandovan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6504429/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The changes in reliability over the life of equipment with duplex and triplex architectures can be compared with the changes in failure rate and repair or replacement rate. Now, if by carefully planning smart repairs with a modern approach with artificial intelligence and the Internet of Things and predictive maintenance, the failure rate can be reduced, the repair or replacement rate increases and failures occur less, then we have controlled the failure. This is a very good goal. While redundancy strategies have been studied in various industrial applications, recent advances in artificial intelligence, the Internet of Things, and predictive maintenance provide new angles for us to explore. To increase the system reliability using support nodes, duplex and triplex architectures with Markov model were proposed. In this paper, we seek to compare the reliability over time in duplex and triplex architectures using support nodes. So that the changes in reliability over time or the life of the equipment can be compared. Always-on systems cause fatigue and carelessness. They also cause errors and failures that are not reliable. So we are looking to give the tired always-on system a chance to rest. The beauty and innovation of the work is that in the time dimension, by carefully planning the maintenance unit using the duplex and triplex backup model architecture, an opportunity can be created to increase the repair or replacement rate. Maintenance teams can control failures and increase reliability through intelligent scheduling and the use of machine learning (ML) and Internet of Things technology. Other benefits include improved system rejuvenation and dynamics. The algorithm design uses a sleep-wake mode of the backup node. If any of the two or three hot standby nodes are damaged, the cold standby node is in service, and the damaged nodes go into recovery mode to be repaired or replaced if possible, and then return to standby. Another important advantage of this method is that it can be put into service without interruption, it can be scheduled with artificial intelligence for smart maintenance and predictive maintenance with the Internet of Things. Prevent failures and improve the repair and replacement rate. Therefore, the reliability of this algorithm is very high. It is widely used in industries and also depends on the interaction between reliability and cost. Artificial Intelligence Cold and Hot Sensors Fault and Failures Machine Learning Redundancy Nodes Reliability Standby Sensors Smart Maintenance Triplex and Duplex Markov Model Full Text Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-6504429","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452146313,"identity":"b0883dc2-bfe5-46d7-8657-719e8814e1d8","order_by":0,"name":"Ahmad reza Zamani","email":"","orcid":"","institution":"Electric Power Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"reza","lastName":"Zamani","suffix":""},{"id":452146314,"identity":"c2326a72-c620-40b4-8146-24e61423a991","order_by":1,"name":"Mohammad Ali pourmina","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYNACAwYGfgYGNoYEKP8AUVokG0jTAtJ1AKiFKGDO3mP26UbBHXnj82efPXhQc5iBv/0A4+EKPFose84Yz84xeGa47Ua6uUHCscMMEmcSGA6eweeeGznGzDkGhxm33WBjk0hgO8zAcIOB4WADEVrsN/cfA2r5d5hBnlgtiRsY0tgkEtsOA0UIaLHsOVYM0pI840Yau0FiXzqP4ZnEBrxazNmbNzPn/Dls2w902MMf36zl5I4fPvwRr8PQBXgYGBjxacCiZRSMglEwCkYBBgAAF55OTxgpugUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6884-0266","institution":"Computer Systems Institute","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Ali","lastName":"pourmina","suffix":""},{"id":452146315,"identity":"30f3c5ac-6807-4179-b42a-23e18571b0a0","order_by":2,"name":"Ramin Shaghaghi Kandovan","email":"","orcid":"","institution":"Computer Systems Institute","correspondingAuthor":false,"prefix":"","firstName":"Ramin","middleName":"Shaghaghi","lastName":"Kandovan","suffix":""}],"badges":[],"createdAt":"2025-04-22 12:52:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6504429/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6504429/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83347183,"identity":"26c703dc-0887-4da4-9376-8d6a8318c8c3","added_by":"auto","created_at":"2025-05-23 12:53:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1985846,"visible":true,"origin":"","legend":"","description":"","filename":"ISIRt8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6504429/v1_covered_da291997-75d0-4999-9f70-b71d4c75b923.pdf"}],"financialInterests":"","formattedTitle":"Improve Reliability Over Time And Repair and Replacement Rates With Two And Three Spare Nodes Using The Duplex And Triplex Markov Model with AI and IoT In Industrial Wireless Sensor Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Cold and Hot Sensors, Fault and Failures, Machine Learning, Redundancy Nodes, Reliability, Standby Sensors, Smart Maintenance, Triplex and Duplex Markov Model","lastPublishedDoi":"10.21203/rs.3.rs-6504429/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6504429/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe changes in reliability over the life of equipment with duplex and triplex architectures can be compared with the changes in failure rate and repair or replacement rate. Now, if by carefully planning smart repairs with a modern approach with artificial intelligence and the Internet of Things and predictive maintenance, the failure rate can be reduced, the repair or replacement rate increases and failures occur less, then we have controlled the failure. This is a very good goal. While redundancy strategies have been studied in various industrial applications, recent advances in artificial intelligence, the Internet of Things, and predictive maintenance provide new angles for us to explore. To increase the system reliability using support nodes, duplex and triplex architectures with Markov model were proposed. In this paper, we seek to compare the reliability over time in duplex and triplex architectures using support nodes. So that the changes in reliability over time or the life of the equipment can be compared. Always-on systems cause fatigue and carelessness. They also cause errors and failures that are not reliable. So we are looking to give the tired always-on system a chance to rest. The beauty and innovation of the work is that in the time dimension, by carefully planning the maintenance unit using the duplex and triplex backup model architecture, an opportunity can be created to increase the repair or replacement rate. Maintenance teams can control failures and increase reliability through intelligent scheduling and the use of machine learning (ML) and Internet of Things technology. Other benefits include improved system rejuvenation and dynamics. The algorithm design uses a sleep-wake mode of the backup node. If any of the two or three hot standby nodes are damaged, the cold standby node is in service, and the damaged nodes go into recovery mode to be repaired or replaced if possible, and then return to standby. 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