Unleashing the potential of digital twins: a new era with aeronautics 4.0      

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A digital thread refers to the continuous flow of data and information related to a product throughout its life cycle, integrating and connecting all aspects of a product’s journey. Within this framework, digital twin technology, an essential element of Industry 4.0, comprises the implementation of a virtual presentation of a physical object, system, or process. It brings together the digital replica not only of the physical attributes but also of the behavioral performance of the physical twin. Methods To achieve a digital thread perspective for aeronautics, a research agenda is proposed, including all breakpoints in the current value chain through a PESTLE Analysis, which defines the value-added areas targeted by this transition to digital technology. An aeronautics 4.0 model Digital Thread Twin Smart Aeronautics D2TSAero is proposed, which provides a new perspective in the aeronautics field by integrating its main ecosystems into an interconnected model made possible by the integration of digital twin agent instances. Proof of Concept A use case that deals with the dependability management of an aircraft fuel distribution system is presented. Based on these results, we can see that the proposed twin model can help in reducing real system parts down time, and it can also improve the management of maintenance across the system life cycle by offering a single source of trust for all stakeholders involved in the digital thread cycle of the real twin. Conclusion A forward-looking perspective on the future of aeronautics with this integrated approach is presented, summarizing all the discussed points and the importance of digital twins in supporting the digitalization of the field. 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F1000Research 2024, 13 :193 ( https://doi.org/10.12688/f1000research.144038.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Unleashing the potential of digital twins: a new era with aeronautics 4.0 [version 1; peer review: 1 approved, 1 approved with reservations] Mezzour Ghita 1,2 , Benhadou Siham https://orcid.org/0000-0003-2683-2780 1,2 , Benhadou Mariam 1 , Haddout Abdellah 1 Mezzour Ghita 1,2 , Benhadou Siham https://orcid.org/0000-0003-2683-2780 1,2 , Benhadou Mariam 1 , Haddout Abdellah 1 PUBLISHED 19 Mar 2024 Author details Author details 1 Engineering Research Laboratory, National and high school of electricity and mechanic (ENSEM) HASSAN II University, Casablanca, Casablanca, Morroco, Morocco 2 Engineering and Research, Research Foundation for Development and Innovation in Science and Engineering, Casablanca, Morocco Mezzour Ghita Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation Benhadou Siham Roles: Funding Acquisition, Investigation, Project Administration, Supervision, Validation, Writing – Review & Editing Benhadou Mariam Roles: Project Administration, Resources, Supervision, Writing – Review & Editing Haddout Abdellah Roles: Funding Acquisition, Project Administration, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Abstract* Introduction The aerospace value chain consists of several processes, and digitizing it first requires an assessment of these processes and their ability to be transferred to the fully integrated digital thread perspective of smart factories. A digital thread refers to the continuous flow of data and information related to a product throughout its life cycle, integrating and connecting all aspects of a product’s journey. Within this framework, digital twin technology, an essential element of Industry 4.0, comprises the implementation of a virtual presentation of a physical object, system, or process. It brings together the digital replica not only of the physical attributes but also of the behavioral performance of the physical twin. Methods To achieve a digital thread perspective for aeronautics, a research agenda is proposed, including all breakpoints in the current value chain through a PESTLE Analysis, which defines the value-added areas targeted by this transition to digital technology. An aeronautics 4.0 model Digital Thread Twin Smart Aeronautics D2TSAero is proposed, which provides a new perspective in the aeronautics field by integrating its main ecosystems into an interconnected model made possible by the integration of digital twin agent instances. Proof of Concept A use case that deals with the dependability management of an aircraft fuel distribution system is presented. Based on these results, we can see that the proposed twin model can help in reducing real system parts down time, and it can also improve the management of maintenance across the system life cycle by offering a single source of trust for all stakeholders involved in the digital thread cycle of the real twin. Conclusion A forward-looking perspective on the future of aeronautics with this integrated approach is presented, summarizing all the discussed points and the importance of digital twins in supporting the digitalization of the field. READ ALL READ LESS Keywords Aeronautics ecosystems, Digital Twin, Digital Thread, PESTLE Analysis, Smart agents Corresponding Author(s) Mezzour Ghita ( [email protected] ) Benhadou Siham ( [email protected] ) Close Corresponding authors: Mezzour Ghita, Benhadou Siham Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2024 Ghita M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Ghita M, Siham B, Mariam B and Abdellah H. Unleashing the potential of digital twins: a new era with aeronautics 4.0 [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2024, 13 :193 ( https://doi.org/10.12688/f1000research.144038.1 ) First published: 19 Mar 2024, 13 :193 ( https://doi.org/10.12688/f1000research.144038.1 ) Latest published: 19 Mar 2024, 13 :193 ( https://doi.org/10.12688/f1000research.144038.1 ) 1. Introduction The rise in competitiveness among market actors from different industrial fields has resulted in an increase in systems engineering complexity and manufacturing process complications. 1 Several years ago, the US Air Force in one of their publications on digital twins highlighted the concept of digital thread. 2 Digital thread was introduced as a part of their framework for digital engineering in order to optimize defense system manufacturing and the engineering phase’s costs and delays. Digital threads were defined in their work as a scalable, configurable, and modular reference framework at the enterprise level for complex systems manufacturing and life cycle management across enterprise hierarchical levels. 3 Based on the developed digital system model, the main purpose of the digital thread is to accelerate the sharing and transparency of authoritative interactions and exchanges of technical data, software, information, and knowledge in enterprise data, information, and knowledge systems in a smooth and controlled manner across the systems lifecycle. Second, heterogeneous and distributed data from different sources throughout the system life cycle are accessible to all system stakeholders and decision makers in order to integrate and transform them into actionable information. 4 This concept contributes significantly to the deployment of end-to-end engineering reinforced by the Reference Architecture Model for Industry 4.0 RAMI 4.0 Vision. 5 RAMI 4.0 is one of Germany’s reference architectures for the implementation of Industry 4.0, across factories. RAMI 4.0, which was proposed by the German Electrical and Electronic Manufacturers’ Association (ZVEI) and recognized by a large number of companies and research groups as a reference framework for the integration of Industry 4.0 within manufacturers to meet smart manufacturing requirements. RAMI 4.0 focus on the development of four main axes: horizontal and vertical integration, human intelligence integration with artificial intelligence and computing capacities, and smart end-to-end engineering. 6 The digital twin that we defined according to our previous exploration of the concept background as a virtual image of living and non-living entities that incarnates real entities with complex internal structures and their behaviors with their evolving physical environment through the use of newly developed and highly connected technologies that take advantage of fusion between physical and virtual worlds within an interactive simulated environment for the establishment of smart data integration platforms and collaborative networks of intelligent and connected agents. 7 Digital threads offer a single source of truth for digital twins, where all data from the real asset life cycle are stored and shared. 8 However, it also presents some main challenges, one of which is discussed in this study. Q1: How to deploy a secure, smart and resilient control command architecture to manage the horizontal and vertical integration of shopfloor and office floor within factories The primary goal of this research is to comprehensively investigate the application and assess the impact of digital twin technology within aeronautical systems. In the aeronautics sector, a prominent transformation is unfolding through the integration of digital threads and twins. 9 These concepts represent an innovative approach to information management and system analysis, presenting profound potential to revolutionize aircraft design, manufacturing, 10 operation, and maintenance. 11 We aim to explore how the implementation of digital twins in aircraft design, manufacturing, maintenance, and operational monitoring can optimize processes, enhance safety, reduce costs, and ultimately revolutionize the aeronautics industry. The remainder of the paper is organized as follows: Section 2 describes the current aeronautics aero value chain and its main activities from a macroscopic perspective, and the PESTELE analysis highlights the main breaking points for the integration of aero 4.0. In Section 3 , the Digital Thread Twin for Smart Aeronautics DT2Aero 4.0, which portrays the new aeronautics value chain and ecosystems from a Digital Thread and Twin perspective, is discussed. Section 4 discusses the implementation of the proposed model with a proof of concept that deals with the implementation of digital twins for an aircraft fuel distribution system. The proposed use case aims to shed light on the potential value-added of digital twins for smart maintenance planning and operation optimization. Sections 5 and 6 summarize the contributions and limitations of the proposed solution and provide a further research axis. 2. Methods 2.1 Aeronautics value chain analysis The aerospace value chain consists of a series of processes, and digitalizing it begins with an assessment of these processes and their potential for integration into the fully integrated, three-dimensional Industry 4.0 perspective of the smart factory. Through Figure 1 . we attempt to frame all the aspects of the value chain. Figure 1. Aeronautics value chain break down and ecosystems overview. The value chain is divided into three main parts: • Upstream activities : These activities involve obtaining the raw materials and components required to manufacture the product. This may include activities, such as procurement, logistics, and warehousing. This also includes a network of influencers, such as experts, universities, and the government, that can have an impact on the evolution of field regulations, standards, and structures in the long and short term. We also consider technology providers as they can present evolving solutions to initial product development phases and customer requirements as they evolve. • Core activities : These involve actual product manufacturing. This may include activities, such as production planning, assembly, and quality control. It includes all the main steps involved in system development and manufacturing from type to instances, as described for the digital thread concept. • Downstream activities : These activities involve obtaining the finished product from the customer. This may include activities, such as marketing, sales, and distribution. We add to these activities operations of reverse engineering, which are part of the new sustainable manufacturing perspective. Four main ecosystems are defined to frame the value chain of the aeronautics industry: development of engineering activities (study, design, research, and development), electrics and wiring (development of electrical systems and harnesses ….), assembly (mechanical equipment, electronic modules, wires and harnesses; structural parts …), and maintenance and repair operations (painting, component, and engine repair…). The mainstreaming of these improvements is achieved by leveraging Industry 4.0, technologies to support the realization, updating, and maturity enhancement of existing processes. 2.2 PESTEL analysis for Aero 4.0 integration Tables 1 , 2 , 3 , and 4 provide an overview of these tools and of the breaking points that we have defined as the value-added workstreams involved in this digital transition. The specification of these workstreams provides a clearer picture of the impact of digitalization on the value chain in the short and long terms. The representation of the current value chain in addition to the PESTEL analysis presented in Table 1 , 2 , 3 , and 4 are used to define the targeted areas of improvement for each ecosystem in the selected business segment and find areas of added value for digital threads and digital twins. Table 1. PESTELE analysis for Maintenance and repair operation ecosystem transition. Phase Breaking point Revised features Toolbox 4.0 Interactions Ecosystem 4: Maintenance and repair operation Operation and maintenance (T) Availability of external expertise Modelling the granularity of system structures - Reinforced learning for collaborative maintenance - Internal Actors (control agents, production team, decision-makers) ( E2 ) Integrated and interconnected management systems Adaptive and dynamic deterioration models - Ontologies and linked data 12 - Actors Value chain Connected site ( S ) Extended downtime (health restrictions) Vertical knowledge sharing - Supervised and unsupervised learning 13 - Customer network (Airline) ( S ) Labor availability Intelligence augmented by Human-Machine interaction - Cobots 14 ( E2 ) Outage and shortages of spare parts and risks of very long replacement cycles Modelling uncertainties - Supply chain 4.0 15 ( S ) Inherent interactions of labour and countries aging rates Collaborative and agile policies for labour management - Social Digital twins 16 ( T ) Complex system structure and complex behavioural analyses Intelligence augmented by Human-Machine interaction - Augmented and virtual reality 17 ( T ) Probabilistic distribution of impact factors and monitoring condition due to the modularity of the system Modelling uncertainties between factors influencing the state of health of systems and machines - Digital twins 18 ( T ) Expanded connected network and integrated security constraints Multiperspective modelling and privacy and security by design - Distributed Ledgers Technology 19 Table 2. PESTELE analysis for Component development and assembly ecosystem transition. Phase Breaking point Revised features Toolbox 4.0 Interactions Ecosystem 2 and 3: Component development and assembly Manufacturing and Production ( T ) Complex and personalized modular systems - Configurable and integration of several aspects 3D simulation and modelling 20 - Actors in the Supplier network ( T ) Integration of uncertainties in system design - Adaptability and modularity of production line resources Additive manufacturing 21 - Actors in the customer network ( T )- System autonomy requirements - Horizontal identification and tracking of systems and components Artificial intelligence (Operations optimization, anomaly detection, Artificial vision, etc.) - Internal Actors (production team, business team, decision-makers) ( E2 ) Shortages of raw materials, resources, spare parts - Cognitive and physical ergonomics integration at the production system level Autonomous robotics ( S ) Highly qualified Labor availability - Semi-automatic or automatic quality control Big data ( S ) Integration of new health and safety measures at work - Flexible and modular workstation - Self-assessment and context sensitivity - Connectivity and technical interoperability throughout the production chain - Integrated inventory management - Real-time KPI visualizations Industrial Internet of Things RFID technologies Lean 4.0 (VSM 4.0, KPI, Kanban, SMED …) 22 M2M communication Augmented and virtual reality Table 3. PESTELE analysis for development of engineering activities ecosystem transition. Phase Breaking point Revised features Toolbox 4.0 Interaction Ecosystem 1: Development of engineering activities Design and Manufacturing (E) Highly variable fluctuations in demand and distribution between different customers around the world Capturing agile and dynamic needs and specifications during the requirements definition process - Geospatial and business intelligence 23 Actors in the influencer network (E) Supplies affected by external constraints (health, politics, etc.) Multidimensional integration of aspects and influences on systems - Artificial intelligence for demand prediction and game theory for balancing different design objectives (T) Adoption of new digital management systems by suppliers and customers Development of semantic and syntactic interoperability within the value chain - Blockchain through AAS 24 , 25 (E) Strict normative requirements regarding environmental impacts Collaborative design and feedback sharing between parties from the design and integration teams and suppliers - Agile design and manufacturing 26 (T) Revolution with regards the to labor demand need for Human Machine collaboration and customization Prototyping and digital design Integrated model simulation and testing Integrated and distributed lifecycle data management - Cyber Security - DLT - Cloud computing ( P ) Heightened concerns regarding national security and the need to protect critical aerospace infrastructure from cyber threats. Digital licensing and authority management - Cyber Security - DLT Table 4. PESTELE analysis for maintenance and repair ecosystem transition. Phase Breaking point Revised features Toolbox 4.0 Interactions Marketing and Distribution (S) Health restrictions and reduced air traffic Customer experience support - Business and geospatial intelligence Internal Actors (control agents, production team, business team, decision-makers) (P) Changes of politics of direct customers and customers of customers Lifecycle Data Governance Integration from end users perspective Active analysis of customer trends Digital twins Expert system and intelligent recommendation system Virtual and augmented reality Cobots and Sculpins Blockchain Natural Language Processing Recycling and end of life (E) Strict and developing standards and requirements for the management of environmental impacts resulting from the life cycle of sector systems Environmental sensitivity Digital twins and advanced simulation 27 - Internal Actors (control agents, production team, business team, decision-makers) (P) Regulatory framework currently being deployed to reduce environmental damage throughout the value chain Tracking life cycle impacts and life cycle costs Carbon free structures - Responsiveness in waste management - Collaborative disassembly - Multidimensional modeling - Collaborative sharing of knowledge on the use and maintenance phase for adaptation, reuse and redesign - Expert system and intelligent recommendation system - Additive manufacturing - RFID technology - Object recognition using artificial intelligence - Cobots - Blockchain Influencer networks 2.3 D2TSAero – Digital Thread Twin for Smart Aeronautics The proposed solution D2TSAero, represented in Figure 2 , consists of three key network elements: suppliers and influencers, digital twins and digital threads, customers, and their customers. Figure 2. D2TSAero – Digital Thread Twin for Smart Aeronautics. Network of Suppliers and Influencers: The first network, namely that of suppliers and influencers, includes all parties with an influence on the operation of the main ecosystems, such as standardization or auditing organizations, which establish domain management procedures and define best practices; government; and its constituents, which manage the domain’s strategic development model and influence the local, national, and international organizational development of ecosystems. Second, we identify all stakeholders involved in the production of the finished product and the supply of the main materials, elementary means for the development of the environment in which the finished product is produced, and the labor that completes this operation. The field is classified as labor-intensive, and technological advances are reducing the number of complex or repetitive operations and helping the workforce improve its skills. The addition of universities and research organizations to this layer is intended to reinforce this aspect and thus continually update the sector’s various missing links for better integration. Each party in the network is represented by its AAS. The AAS concept was introduced by the European Platform 4.0 coalition to ensure the deployment of Industry 4.0, within factories, and the implementation of the digital twin concept. It is the digital image of the entities within the virtual world, represented by the primary element encompassing the identification and domain information models of the entities, and the secondary elements constituted by the communication interfaces and management component enabling the various models and subsystems of the AAS to be managed. Network of Digital twins and Digital Thread: The second network is that of the digital twins and digital value chain and covers the new development model for the various production environments and ecosystems involved in the complete manufacturing of the finished product. Each ecosystem is represented by a global digital twin. Digital twin ecosystems communicate with each other and with the other two networks via dedicated application programming interfaces. The basic architecture of the digital twin ecosystems was developed according to the architecture shown in the figure and in compliance with the two standards ISO/IEC 30141:2018 for an Industrial Internet of Things reference architecture and ISO for the development of digital twins within manufacturing industries. The choice between these two standards is driven by our motivation to propose a generic and scalable architecture. We have added a security layer to the base layer, made up of two actors: the first is that of intelligent contracts, developed on the basis of the description of network agents and their interactions, and monitored by intelligent encryption and artificial immunity algorithms; the second actor is based on Distributed Ledgers technology for managing exchanges and transactions between entities in the digital world. The interaction between these two elements makes it possible to build an immune interface between the different agents of the architecture given the criticality of the domain, despite the need to ensure horizontal and vertical integration between its players. The basic twin architecture is composed of two layers: the digital twin layer and the service layer, which comprises three services: collaborative maintenance management, exploration of ecosystem entities, and quality management and continuous improvement. The digital twin layer consists of twins and industrial entities as well as a workforce contributing to the creation of added value, its information perception and contextualization layer, and the intrinsic twin layer encompassing information models, physical entity simulation models, and twin functionality. Cross domain functions: We also defined cross-domain functions for all parts of the architecture, such as security management, license management, and digital authorities. Communication between these different layers is ensured by communication protocols and mediating communication layers such as OPCUA, MT Connect, Automation ML, and OPCUA. The choice of protocol or integration depends on several criteria, including technical interoperability, cyber security, and deployment costs. The integration of standard data formats and information models within the architecture for semantic and syntactic interoperability is ensured by AAS directories developed based on an agile and collaborative engineering process provided by twin-type simulation, cloud platforms, and virtual prototyping. Within the digital value chain model, logistics and reverse engineering are integral to the ecosystem development process. The digital twin’s reactivity to waste through optimization functionality enables the management of the non-value-added produced by each process. This non-value-added is communicated to the collaborative maintenance and continuous improvement departments for in-depth analysis, supported by the network of decision-makers. All modifications and improvements produced by the synergy of artificial and human intelligence were communicated to Ecosystem 1 and integrated into the new instance model of the twin types, thus facilitating reverse engineering and digitizing the process of testing new structures for improved performance. Meanwhile, waste management is handled by process twins, which integrate the reverse value chain into the overall ecosystem management. Network of Customers and their customers: The last layer of the architecture is that of customers and customers, represented by airlines, international manufacturers, and end-users who are customers of airlines. From a Lean management perspective, the aim is to create an interactive, dynamic interface with this network via the services in order to manage the entire development and engineering process in an optimized, end-to-end way, but also to offer this network the possibility of benefiting from the integration of digitalization tools within the value chain for triple synchronization of costs, deadlines, and quality. 3. Proof of concept 3.1 Decision Making Problem context description The simulation is based on the model of an aircraft distribution system proposed by Ref. 28 for the monitoring of safety-related condition indicators, as shown in Figure 3(a) . The choice of this particular use case was motivated by the first area of concern discussed previously, which extended the proposed framework discussed by the different developed DT instances and types. The remainder of the use case is based on the analysis of the dataset proposed for this system, which is considered to be produced data from the real system. Figure 3. Real (a) and Virtual twin for Fuel distribution system (b). The proposed system is inspired by the system described in previous sections and is composed of three main components: tanks, five main pumps and valves, and a refuelling point. The central tank is connected to two valves that control the amount of fuel that flows to each of the two engine lines. The lines are connected to the front and rear engines by a main pump and a valve that controls, in addition to the pump, the flow of fuel through the engine. Each line is connected to a main tank provided with fuel from the central tank by means of a main pump and a valve for flow control. Different flow, pressure, temperature, and level sensors are installed to reinforce system safety and report measurements for flight control and for further analysis. A refuelling point is activated when the level of fuel crosses a minimum within the central tank. DT instances have the main mission of adapting system behavior to environmental changes by optimizing individual assets and collective performances. The proposed system consists of a set of complex systems that exhibit particular properties and models that relate to the areas of interest for decision makers and users’ operational and business teams. The two decision-making problems interact to achieve the two main defined sub-goals for DT agents: smart maintenance scheduling and control optimization. Decision-making problems are defined through States, Actions, Rewards and State-Action Transition matrices. In the context of this paper, we focus on defining smart maintenance scheduled through the proposed solution. Operation sequence control optimization will be included through the rewarding system and detailed in further work. DT Types work in parallel to help the instances perform best. DT Types continuously learn to reinforce their instances’ decision-making capabilities. For this purpose, we developed a simulated environment that helps simulate faulty and healthy scenarios from previous experience, but also results from random noise and conflicting situations. Real scenarios exploited from the proposed dataset for the use case were augmented with a newly developed dataset that aimed to integrate security threats affecting the three physical, cyber, and networking parts of each asset, particularly for the measurements and control system. As highlighted previously, we want to explore the situation of coordinated learning to resolve conflicting goals by integrating both security and safety concerns. Coordination completes the adaptive and smart monitoring loop of the system by evaluating the correlations between safety and security risks for implemented safety equipment and measurement systems, such as sensors and valves. 3.2 DT Types models • Centrifugal Pump Hydraulic Model for Type The model was developed on the basis of the Pump Head and torque characteristic curve data interpolation according to the empirical model of the head and torque and pump Affinity Laws based on Eq. (1) and Eq. (2) , respectively. (1) Hp = h 1 Q 2 w + h 2 Qw + h 3 w 2 Q (2) T p = k 1 Q 2 w + k 2 Qw + k 3 w 2 Q Q Q 0 = w w 0 ; P P 0 = Q Q 0 3 ; H H 0 = Q Q 0 2 Hp , Q , w : head, Flow rate and speed for pump Q 0 , P 0 , H 0 : nominal head, Flow rate and speed for pump Coefficient approximation was performed on MATLAB Curve Fitting Tool according to selected operational points from the chose centrifugal pump for nominal speed: w 0 = 120 rad / s h 1 = 4.755 e + 05 h 2 = 6.712 e + 06 h 3 = 2.574 e + 06 k 1 = 90.64 k 2 = 66.02 k 3 = 309.4 • Driver model for Pump Speed control model for Type The dynamics of the electrical driver are modelled by speed variation across the shaft and pump torque evolution as a load for the motor represented by Eq. (3) and Eq. (4) , respectively, and loss expressions are considered to reproduce real motor operation according to Eq. (5) . The regulation pump speed through the diver was introduced by a PI regulator on the motor torque represented by Eq. (6) . (3) J ∆ d ∆ ω dt = 2 × T e − T p − D ω × ω m − ω 0 (4) T e = V × U × sin δ x × ω (5) D ω = ξ × ω s × J ∆ × P max 2 (6) u s = K b W s − Y s + 1 T i s W s − Y s T e : Pump drive torque loss J ∆ : Intertia for pump drive D ω : Damping factor T p : Pump torque Y s : Process variable u s : Control output W s : Set point for control To adapt the system to different operating scenarios, PID parameters were tuned according to a PSO Loop with system models of the plant. At each new goal plan for the agent, the PID function is called to provide agents with new optimized input variables. The activation of the Loop is Controller with a goal flowchart. T i = 0,008 K p = 0 , 01 b = 1 • Tank model for Type The dynamics of the tank are modeled by the tank volume, flow, and level evolution during the simulation according to Eq. (7) , Eq. (8) , Eq. (9) and Eq. (10) . for level, fluid pressure, and flow, respectively. The flow at the outlet of the tank depends on the valve characteristics and the control signal to the valve-opening position. Eq. (11) describes the model tank level as an output variable for a control loop, which is discussed further for system fault modeling. (7) Q in = Q + F × ⅆ h ⅆ t (8) h t = ∫ 0 T Q in − Q A dt (9) p in + Qin 2 2 × φ × s 2 = p 0 + φ × g × h − h 0 (10) Q = A Y valve × h (11) h t = u t × 100 u t = Q in t − Q in Steady − State Q in Steady − State 3.3 Field Interrogation, Optimization, Prediction and Learning As Described earlier, the two DT networks of type and instance collaborate to achieve defined smart maintenance planning. To achieve this goal, DT agents exploit their acquired capabilities and assigned roles by default. One of the main roles deployed by instance and type is Online and Offline learning. The learning model for the two networks is defined by Reinforcement Learning RL quadruple S t A t T s R t agent states within the environment, Actions, Transition probabilities, and rewards. - DT agents states within the environment: Agent States within the environment are defined by three dimensions that describe the main factors that orient agents’ decisions and actions. The first dimension concerns Highly Critical Assets HCA states that can be considered as the main influencers of the overall system performance. The second dimension is defined through the Asset Health Index, which portrays asset status by the combination of main asset deterioration and health evolution models and the impact of modifiers on this status. Three modifiers were considered: health, reliability, and operational modifiers. The third dimension concerns failure evolution, which we define as the failure rate that can be linked to both deterioration modes and health index evolution to reduce the state-space dimension. Inspired by Ref. 29 , we define critical asset states as New, Deteriorating, Deteriorating critical, under PM, available to adjust, and failed. Asset’s criticality code that will be included in the Functional Safety Sub model will be assigned to assets based on a multicriteria analysis of asset health deterioration impact degrees, and this will be detailed throughout our further works. Several health index evaluation models have been proposed in the literature; for the purpose of this paper, and in order to reduce problem definition complexity referring to asset performance and deterioration modes, we define the Asset Health Index (AHI) H i t through flow and efficiencies, as proposed in the framework developed by. 30 The choice of this framework is motivated by our interest in integrating different modifier factor impacts on asset performance and index-wide use in the aviation domain. The explicit representation of the states according to the health index and failure rates is given by Eq. (12) , Eq. (13) and Eq. (14) . The failure rate in the context of this study was assumed to follow a probabilistic Weibull Distribution. (12) H i t = A e Φ t F E t F L t F R t F H t H i t = 0.5 S i ∈ Normal 0.5 < H i t ≤ 2.05 S i ∈ Available to adujst 2.05 < H i t ≤ 4.05 S i ∈ Under PM 4.05 < H i t ≤ 6.05 S i ∈ Deteriorating 6.05 < H i t ≤ 8.05 S i ∈ Deterioratin g Critical H i t = 10 S i ∈ Failed (13) λ j t = ∑ 1 P ∑ 1 D λ i d t (14) λ i d t = c L β i , 1 d η i , 2 d β i , 1 d t β i , 1 d − 1 + β i , 2 d η i , 2 d β i , 2 d t β i , 2 d − 1 S i : Asset state i at instant t. F R t , F E t ,, F H t , F L t : Health, load, reliability, and environmental factors λ j t : Failure rate for asset i at time t. β i , 1 d : Weibull distribution slope for Asset I. η i , 2 d : Weibull distribution scale for Asset I. - DT agents rewards: To evaluate their performance in addition to asset state transitions, DT networks receive a reward at each decision epoch that helps them progress with their action plans through their operation. Agent rewards are defined by the maintenance policy value trajectories. The agents’ decision epochs are defined by several decision episodes at each decision episode agent across various states that are characterized by their values and impacts on maintenance value trajectory optimization. The usage costs of an asset are defined by Eq. (15) . C O , C e n e r g y , C m and C S represent the operation, energy, maintenance, and out-of-service costs, respectively, in Eq. (16) , Eq. (17) , Eq. (18) and Eq. (19) . This cost was subtracted from the asset profit value to constitute the reward function represented in Eq. (20) . These costs are defined as the cumulative usage phase costs and reward for each agent within the network for each decision epoch. All usage-phase costs are considered in this case study to apprehend the benefits of the proposed architecture. The DT agent reward is defined by Eq. (21) . Safety and satisfaction functions are represented by Eq. (22) and Eq. (23) . (15) f obj = VP i T − ∑ 1 Tf Co T + Cm T + C S T × 1 1 + r T (16) Co T = ∑ 1 Tf C energy T + C Supervision T (17) C energy T = φ × g × H n T × Q n T 366 × η nm T × η np T × T × C u . (18) Cm T = ∑ 1 Tf a k T ∑ j = 1 Np ∑ i = 1 Np C mj i T × f mj i T + b k T ∑ j Nf C test i T × f test i T + c k T ∑ d = 1 D ∑ i = 1 Np C d j T × λ d j T (19) C S T = ∑ 1 Tf d Out of service T × C Out of service T (20) f Safety _ pump = ∑ 1 Cp maximum 0 C p 2 (21) f Satisfaction _ pump = ∑ 1 C T maximum 0 C T 2 − 200 f obj > 1 and S i t = “ Deteriorated _ Critical ” − 100 f obj = 1 + 100 f obj < 1 − 200 f Safet y pump 0 + 50 f Safety _ pump = 0 − 200 f Satisfaction _ Tank 0 + 100 f Satisfaction _ Tank = 0 (22) r T = a f obj T + b f Safet y pump T + c f Satisfactio n pump T (23) R = r T + γ r T + 1 + γ 2 r T + 2 + ⋯ + γ K − T r K Within the cost function, we define three coefficients for preventive, corrective, and repair, and test costs a k , b k , c k respectively. - DT agents Actions: Action space consists of five main actions and their derivatives, which are defined through ISO 14624 definitions of major maintenance activities. Two categories are defined: the first includes one action, which is No Maintenance performed, and the second category, which focuses on different maintenance actions, includes four maintenance activities. Check and adjust or Minor Maintenance, which consists of the execution of different inspection operations while maintaining asset operation; the second maintenance action within this category is Preventive Maintenance, which includes the set of planned checks for preventing asset operation and the adjustment of working parameters, the third action includes Corrective Maintenance actions that result from asset failure during its operation as a critical result for agent control. CM actions are defined according to the resulting failure modes, its causes, and effects, and their definition by architecture agents’ networks will be detailed further. The last action within this category is replacement, which transits assets to a new state and is considered the worst case for DT Types and instances network performance evaluation. Algorithm 1 represents the agent’s capability for anomaly prediction and field interrogation based on the proposed model for Health Index estimation. The proposed algorithm processes time-series data related to asset conditions, extracts features, and uses a classifier to identify anomalies or normal behavior for each asset. It updates the failure probability and behavior label accordingly, and computes certain metrics based on the extracted data. Algorithm 1. DT instances agent capability for anomaly prediction and field interrogation. Input: Raw Data Streams time series sequences for asset i P x 1 t P x 2 t … … P xn t Assset criticality code for asset i C i c l : window lenght Output: Behaviour label y out ̂ and corresponding deterioration mode for Maintainable items MI F i k : Asset i estimated failure probability at step k S HCA : HCA State Initialization 1: S HCA ← S New 2: λ i k ← λ i k = 0 3: F R ← 0 4 : F H ← 0 5 : F l ← 0 6: for k < T 7 for i in 1 n 8: Extract data stream sequence for condition varibales according to window size l 9: Construct Time series Features Matrix and generate condition indicators for asset 10: Classify behaviour and determine extrcated sequences class based on selected classifier If anomaly mode i detected λ i k ← ∑ 1 J ∑ 1 D λ I d k y out ̂ ← C NORMAL m = i 11: Else λ i k ← 0 S HCA ← Normal Behavior y out ̂ ← C NORMAL m = 0 12: End 13: End 14: Compute F H ← ∏ 1 H f 1 H F R ← ∏ 1 R f 1 R F L ← ∏ 1 L f 1 L Add rule to set of behaviours rules and archive new scenario Finally, it adds the computed rule to a set of behavioral rules and archives the scenario for further analysis. Classification is performed based on a model pretrained by DT Types using a case base of healthy and faulty simulation results. Algorithm 2 DT type capability for smart maintenance planning and action optimization based on RL logic. Algorithm 2. DT-Types Learning algorithm for Smart Maintenance Planning. Input: States H ik : Asset i health index at step k s ik : Asset i Status at step k λ ik : Asset i failure rate at step k Actions NM : No Maintenance Actions; PM : Preventive Maintenance; CM : Corrective Maintenance; RA : Replacement Action; AA : Minor Maintenance and Adjustment Actions Action Execution interval t start t end Output: Rewards R = r k + γ r k + 1 + γ 2 r k + 2 + ⋯ + γ K − k r K Initialisation: 1: R ← 0 2: r k ← 0 3: S i 0 ← S it //Random states for health index, status and failure rate extracted from model simulation at instant t 4: k ← 0 5: Repeat: 6: for k < T 7: a ← π a //Actions Sequence according to chosen maintenance policy 8: S ik R ← DT S ik a //Asset states and rewards according to Asset shadow feedback from the execution of action a at step k 9: R ← r k + γ r k + 1 + γ 2 r k + 2 + ⋯ + γ K − k r K 10: Update Q s a according to RL learning algorithm 11: S ik ← S ′ ik 12: Until s is terminal 13: Until criterion is reached Algorithm 3 shows the DT-Instances Learning algorithm for Proactive Decision Making with the integration of collaborative learning between agents through the exploration of similar cases based on the agent’s own experiences and peers’ experiences. Algorithm 3. DT-Instances Learning algorithm for Proactive Decision Making. Input: States RUL : Estimated Asset Remaining useful life H ik : Asset i health index at step k F ik t : Asset i failure probability at step k Actions Actions Sequences from heuristic search Output: Rewards R = r k + γ r k + 1 + γ 2 r k + 2 + … + γ K − k r K Initialisation 1: Q t s a and H t s a randomly 2: F ormulate proposal join p description scope //Description depends on failure probability and condition, scope with criticality code of neighbours 3: Repeat: 4: for each cycle step k: 5: Compute Sim p c and Cos t adaptability p c //Explore for similar cases within knowledge base and according to network agent experiences 6: if there is a case c that can be reused: 7: Compute H t s a with action policy proposed by the selected cases 8: a ← x k . I k . I + I − 1 //Set policy based heuristic search extracted actions 9: else 10: Select action a using ∈ − greedy policy 12: Excute policy and observe R s a , S ′ ik 13: Update Q t s a 14: S ik ← S ′ ik 15: Until s is terminal 16: Until criterion is reached In the context of the proposed use case, five runs with five operating points for each run were simulated to represent healthy conditions of the system. Faulty conditions were simulated using MATLAB by injecting faults at each epoch and their combination through the introduction of a Failure_Scenarios_Simulation () function that can be manipulated by system users to test, simulate, and generate ensemble fault data stores. Table 5 presents the experimental details for the heathy scenarios with different loads. We assume the initial condition that assets have been operating for 100h with 20 start-ups of the Front and Rear Pumps. The estimated flight time is considered to estimate the operational time of assets for the modifier estimation. Table 5. Healthy scenarios for pump unit. Behavior Class Runs Time interval for Scenarios Simulation Estimate Flight time for Asset Age_es 0-500 500-1000 1000-1500 1500-2000 2000-3000 Healthy Behavior 1 (50,120) (120,120) (120,120) (120,120) (120,120) 10h (120,120) (120,120) (120,120) (120,120) (120,120) 9h (50,50) (50,120) (50,120) (50,120) (120,120) 12h (120,120) (120,200) (120,200) (120,200) (200,200) 10h (50,50) (50,50) (50,50) (50,50) (50,50) 10h 2 (120,200) (200,200) (200,200) (200,120) (120,200) 12h (120,120) (50,120) (50,120) (50,120) (50,120) 12h (50,50) (50,120) (50,120) (50,120) (50,120) 9h (120,120) (200,200) (200,200) (200,200) (200,200) 10h (50,120) (50,200) (120,200) (120,200) (200,120) 10h 3 (60,60) (200,60) (200,120) (200,120) (200,200) 12h (180,180) (180,180) (180,180) (180,180) (200,200) 12h (200,200) (200,200) (200,200) (200,200) (50,120) 10h (50,120) (50,200) (50,200) (50,200) (200,200) 10h (200,180) (200,200) (200,200) (200,200) (200,200) 12h 4 (200,60) (200,120) (200,120) (200,200) (200,200) 9h (120,60) (120,120) (200,200) (200,200) (200,200) 9h30 (200,120) (200,200) (200,200) (200,200) (200,200) 10h (60,60) (60,60) (60,60) (120,200) (200,200) 12h (120,60) (180,180) (180,180) (180,180) (180,180) 10h30 5 (180,200) (200,120) (200,120) (200,120) (200,120) 12h (180,180) (120,200) (200,200) (200,200) (200,200) 9h (60,120) (200,120) (200,120) (200,120) (200,50) 12h (200,180) (200,180) (200,180) (200,200) (50,120) 13h (50,120) (50,120) (50,120) (50,120) (200,200) 9h 6 (120,120) (120,120) (120,120) (120,120) (120,120) 9h (50,120) (50,200) (50,120) (120,120) (50,120) 10h (200,120) (120,120) (50,120) (50,120) (120,120) 12h (120,200) (120,200) (120,200) (120,200) (200,200) 12h (120,120) (50,50) (50,50) (50,50) (50,50) 10h Three main critical failure modes for assets, as well as one incipient and intermittent failure mode, were simulated to test different scenarios. Faults are modelled within MATLAB/SIMULINK using different fault models defined by StartTime, Duration, Fault_Value_Set, and Fault_Sevirity_Category. This section introduces Failure_Scenarios_Simulation () in MATLAB for the discussed use case. The fault mode sets are represented by different gains from Eq. (24) – (30) , respectively. (24) G NOI = 0.001 0.1 (25) G AIR = 0.1 0.01 ∗ u max 0.1 0.008 ∗ u max Shaft (26) G AIR = 0.1 0.01 ∗ u nominal 0.1 0.008 ∗ u nominal Instruments (27) G PED = 0.25 0.5 0.75 (28) G ELU = F min Leak F max Leak (29) G STD = − 2 0.2 0 STD Casing G STD = 0 − 1 STD Shaft (30) G SER = − 1 0.5 1 Table 6 presents the scenarios for the faulty behavior of the pump. Security faults arising from security threats are also considered. Table 6. Faulty scenarios for pump unit. Behavior Class Runs Time interval for Scenarios Simulation Estimate Flight time for Asset Age_es 0-500 500-1000 1000-1500 1500-2000 2000-3000 Faulty Behavior 1 NOI, IN NOI, IN NOI, IN 10h AIR, IN AIR, IN AIR, IN 9h SER, C 12h SER, C SER, C ELU, C 10h ELU, C PDE, IN PDE, IN 10h 2 SER, S 12h NOI, IM NOI, IM PDE, IN PDE, IN PDE, IN 12h AIR, IN AIR, IN 9h NOI, IM 10h ELU, S ELU, S 10h 3 SER, S 12h SER, S SER, S SER, S NOI, S NOI, S 12h NOI, S 10h STD, S 10h BRD,S BRD, S BRD, S BRD, S BRD, S 12h 4 NOI, IM 9h NOI, IM NOI, IM 9h30 PDE, IM PDE, IM PDE, IM 10h STD, IM 12h STD,IM STD, IM BRD, IM BRD, IM BRD, IM 10h30 5 AIR, IN AIR, IN 12h SER, S SER, S SER, S 9h ELU, S 12h PDE, S PDE, S 13h SER, Sh SER, Sh 9h 6 AIR, Sh 9h NOI, Sh NOI, Sh NOI, Sh 10h AIR, Sh AIR, Sh 12h AIR, Sh AIR, Sh STD, Sh STD, Sh STD, Sh 12h BRD, Sh BRD,Sh BRD, Sh BRD, Sh BRD, Sh 10h The Weibull parameters estimated from a bibliographic review of aircraft fuel distribution reliability data enable the estimation of the probability parameters for different failure modes of the driven pumps within the system and their subunits. According to the Table above, α 1 and β 1 , α 2 and β 2 are represented for the early and wear-out life phases of the pump unit Eq. (31) to Eq. (34) . (31) β 1 = 0.84 0.00 0.83 0.84 0.80 0.82 0.00 0.88 0.00 0.00 0.88 0.00 0.88 0.00 0.78 0.76 0.75 0.82 0.00 0.73 0.67 0.73 0.00 0.73 0.73 0.00 0.00 0.73 0.00 0.88 0.00 0.00 0.79 0.79 0.70 (32) β 2 = 1.10 0.00 1.13 1.15 1.14 1.15 0.00 1.09 0.00 0.00 1.08 0.00 1.03 0.00 1.13 1.10 1.11 1.13 0.00 1.07 1.13 1.13 0.00 1.09 1.10 0.00 0.00 1.03 0.00 1.08 0.00 0.00 1.02 1.10 1.08 (33) α 1 = 1695 0.00 652 311 1500.5 862 0.00 563.0 0.00 0.0 172 0.00 408 0.00 770.5 101.0 62 496 0.00 650 102 311.2 0.00 108 325 0.00 0.00 105 0.00 310 0.00 0.00 400 102 64 (34) α 2 = 2404.8 0.000 1000 4213 2100 1000 0.000 1260.0 0.000 0.000 1456 0.000 1145 0.000 4192.0 2370 1372 2370 0.000 1352 760.3 2360.8 0.000 1600 4013 0.000 0.000 520.3 0.000 1630 0.000 0.00 1243 1023 642 The proposed reward function of the system was calculated based on the estimated maintenance costs of each subunit and its failure modes. The PM, CM, and RM costs for the subunit discussed in the context of this paper are presented in Table 8 , and the failure mode costs and their respective necessary PM, CM, and RM duration are presented in Tables 7 and 9 . Table 7. Failure modes description. Category Failure Modes Weibull Parameters estimate Shaft Sh Impeller IM Seals S Casing C Instruments IN Critical BRD Breakdown α 1 1695 563 770 311 β 1 0.84 0.88 0.78 0.73 α 2 2404.8 1260 4152 2360.8 β 2 1.10 1.09 1.13 1.13 η 0.3 0.3 0.3 0.3 PDE Parameter Deviation α 1 101 310 β 1 0.76 0.88 α 2 2370 1630 β 2 1.10 1.08 η 0.3 0.3 ELU External Leakage α 1 652 62 108 β 1 0.83 0.75 0.73 α 2 1000 1372 1600 β 2 1.13 1.11 1.09 η 0.3 0.3 0.3 STD Structural Deficiency α 1 311 172 496 325 β 1 0.84 0.88 0.82 0.73 α 2 4213 1456 2370 4013 β 2 1.05 1.08 1.13 1.10 η 0.3 0.3 0.3 0.3 Degraded AIR Abnormal Instrument Reading α 1 1500.5 400 β 1 0.8 0.79 α 2 2100 1243 β 2 1.14 1.02 η 0.3 0.3 NOI Noise α 1 862 408 650 102 β 1 0.82 0.88 0.73 0.79 α 2 1000 1145 1352 1023 β 2 1.15 1.03 1.07 1.10 η 0.3 .0.3 0.3 0.3 Incipient SER Minor in Service Problem α 1 102 105 64 β 1 0.67 0.73 0.70 α 2 760 520 642 β 2 1.13 1.08 1.08 η 0.3 0.3 0.3 Table 8. Subunit maintenance costs and duration parameters. Subunit Costs PM Costs CM Costs RM Costs Tests T PM j T CM j T RM j T TEST j Casing 100 150 5000 150 10 15 72 11 Impeller 180 225 4000 140 8 24 72 12 Shaft 250 355 10000 200 8 24 48 12 Seals 80 120 1000 100 5 12 18 6 Instrument 50 155 2000 100 5 12 15 4 Valve 30 60 5000 120 4 8 15 4 Driver 200 250 20000 150 9 20 72 18 Tank 100 155 10000 140 4 18 72 15 Table 9. Subunit Failures Costs for Failures modes discussed. Subunit C 1 j C 2 j C 3 j C 4 j C 5 j C 6 j C 7 j Casing 1200 750 950 1150 560 580 250 Impeller 4000 1450 2100 3120 1200 850 450 Shaft 5550 2850 1550 2400 1350 1050 550 Seals 2000 1150 550 500 750 620 120 Instrument 1550 1200 980 300 450 330 100 An example snapshot of the standard fault definition document for the fault submodel of digital twins in JSON format according to the standard definition of fault properties is presented in Table 10 . The proposed type of fault model aims to fulfil the concept from digital thread to have a standard and sharable definition of maintenance main concepts that can be used by the DT network but also its stakeholders. Table 10. Failure_Standard_Model for Abnormal Instrument Reading Deterioration mode. {"Failure_SM": [ {"Failure_ID_code ": "PT001/FM001/F", "Failure_ID_version": "001", "Failure_ID_revision": "1", "Failure_ID_IRDI": "PT001/FM001/F#001" "Failure_ID_Preferedname": "Failure Type 1 Identifier for pump Type 1", "Failure_ID_Symbol ": "", "Failure_ID_definition": "the present identifier serves as a unique tag for instances of failures of type 1 ", "Failure_ID_unit": "na", "Failure_ID_datatype": "STRING_TYPE", "Failure_ID_ValueList": "", "Failure_ID_ActualValue": "", "Failure_ID_UnitCode": "", "Failure_ID_ReferenceStandard": "ISO 14624", "Failure_ID_DateofRealisation": "13/04/2020", "Failure_ID_DateofLastUpdate": ""} {"Failure_breakuptime_code ": "PT001/FM001/T", "Failure_breakuptime_version": "1.0", "Failure_breakuptime_revision": "1", "Failure_breakuptime_IRDI": "PT001/FM001/T#002", "Failure__breakuptime_Preferedname": "failure Type Breaktime", "Failure_breakuptime_Symbol ": "", "Failure_breakuptime_definition": "it represents calculated time interval from the occurrence of the failure type for which similar assets with the actual failure type may cross a critical threshold value ", "Failure_breakuptime_unit": "seconds", "Failure_breakuptime_datatype": "REAL_TYPE_VALUE", "Failure_breakutime_ValueList": "]0 ∞`[", "Failure_breakuptime_ActualValue": "1800", "Failure_breakuptime_UnitCode": "", "Failure_breakuptime_ReferenceStandard": "", "Failure_breakuptime_DateofRealisation": "13/04/2021", "Failure_breakuptime_DateofLastUpdate": ""}, {"Failure_Mode_code ": "PT001/FM001/AIR", "Failure_Mode_version": "001", "Failure_Mode_revision": "1", "Failure_Mode_IRDI": "T001/FM001/AIR#003", "Failure_Mode_Preferedname": "Failure Modes ", "Failure_Mode_Symbol ": "AIR", "Failure_Mode_definition": "encountered failure modes Abnormal Instrument Reading for pump type 1 through its life cycle", "Failure_Mode_unit": "", "Failure_Mode_datatype": "STRING_TYPE", "Failure_Mode_ValueList": "", "Failure_Mode_ActualValue": "Active", "Failure_Mode_UnitCode": "", "Failure_Mode_ReferenceStandard": "OREDA", "Failure_Mode_DateofRealisation": "3/04/2021", "Failure_Mode_DateofLastUpdate": "3/04/2021"}, 4. Simulation Results The simulation was executed based on the scenarios proposed in the previous sections. A set of six runs with 30 healthy scenarios and a combination of 30 faulty scenarios were proposed for the discussed use case to apprehend both DT benefits for the simulation of the systems and for testing different scenarios for DT agents. Simulations have focused on both normal and abnormal behavior of the system to work as an interface with the real twin and its stakeholders. Figure 4 (a), (b), (c), and (d) present the simulation results for the flow, pressure, head, and pump shaft torque in a normal scenario. Figure 4. Pumps dynamic head model simulation results Scenario 1 (a) Pumps out flow model simulation results for Scenario 1 compared to engine consumption flow (b) Pumps Shaft speed model simulation results for Scenario 1 (c) Pumps Pressure model simulation results for Scenario 1(d). Figure 5 (a) and (b) present the tank volumes and temperature evolution for the Central, Rear and Front tanks, respectively. Figure 5. Rear Tank and central tank level model simulation results for Scenario 1 (a) Rear Tank and central tank Temperature model simulation results for Scenario 1 (b). Figure 6 and 7 (a) and (b) represent the simulation for some of the presented failure modes, such as the NOISE failure mode, BDR of pumps, and Structural Deficiency of the shaft that is simulated by the introduction of wear out into the shaft-produced torque. Figure 6. BRD Fault Simulation for Rear Pump Activation of auxiliary Rear Pump as a result to the failure of Rear Pump equipment. Figure 7. Rear pump Noise Failure mode simulation for Speed instrumentation and safety equipment(a) Rear pump Noise Failure mode simulation for Flow instrumentation and safety equipment (b). Auxiliary pumps are activated as a result in order to reproduce real system operational logic and dynamics. As we can see throughout the simulation with the introduction of noise into the speed sensor, deviations are reported to different system controllers that are impacted by the output of the speed sensor as instance flow control. This causes the entire system to deviate from its recommended nominal operating limits, which challenges both control logic and other safety equipment such as installed control valves and throttles. The labeled data of the simulated faulty and healthy scenarios are exploited for system behavior classifications and system state reproduction for the type agent’s decision-making support. Simulation outputs were used to compute the asset health index, health modifiers, reliability modifiers, load modifiers, and failure rate estimation for failure story development. Failure stories are used by DT Instances for proactive decision-making and efficient maintenance policy selection in the event of abrupt failures. Figure 8 introduces the DT Instances interface for field interrogation, and Figure 9 introduces the interface for action selection and simulation based on selected policies. Figure 8. DT Instances interface for field interrogation. Figure 9. DT Type interface for actions selection and simulation based on selected policies. 5. Discussion Simulation results for the use case showed that the proposed architecture can be used for both online and advanced offline learning because of the twin’s adaptability to the changes in environments that are either simulated or detected by the operation of the real system. The developed virtual environments for DT type agent learning and training can be easily exploited and adapted to users’ requirements, and learning can be transferred among different DT Types with various context constraints and conditions that enhance both DT types and instance awareness and reactivity. The introduction of the mediator agent for DT maintenance application use cases can help reinforce the testing of different policies and reduce the exploration space of the agents while reinforcing their information on the environment and other agents. Fusion between machine learning-based approaches and statistical analysis of faults by the introduction of estimations of failure rates based on similar equipment historical data can expand the scope of application of Digital twins through the maintenance field by taking advantage of approaches familiar to maintenance management teams and shared among different stakeholders, and can also help to establish real-time tracking of more advanced maintenance indicators for critical industrial domains and applications. In this study, we hypothesize that the failure rate obeys a Weibull distribution with two-parameter estimation, and further studies will focus on the development of a database that serves as training samples for estimation, transmitting the focus on a larger scope that affects each failure mode with an adequate probability distribution according to a fleet of instances in different domains. The proposed virtual environment can be enhanced by a direct connection with different types of engines and pumps. In this study, we focus on centrifugal pumps, electrical motors, Fuel Tanks, valves, and sensors as safety equipment failure modes. The focus is on centrifugal pump use, and safety systems were integrated into pump unit boundaries, which may have limited the overall impacts of fault simulation on overall system behavior changes. Security faults were integrated as a part of the noise in measurement systems, and further work will concentrate on the effective integration of these failures as a part of the smart supervision system introduced by mediator agents, as they constitute an integrated part of the system capability model. The broader scope of the architecture was not developed through the paper as the use case was focused on an open ecosystem to apprehend the value added of the proposed model to the operation and maintenance perspective and provide an overview of how the concept of instances and types introduced by digital thread can facilitate the introduction of digital twins into the field and provide an improvement with regard to discussed break points by the PESTELE analysis presented. 6. Conclusion An approach based on the combination of digital thread and digital twin concepts and advanced simulation is proposed to develop a distributed and smart architecture for digital twins. Modeling, which is considered one of the main elements of the basic foundation of digital twins, is apprehended by a network of smart agents that, in addition to being structured and flexible, enables the integration of the organization concept, which is a major constraint for modern digital twin systems’ autonomy and interoperability. Reinforcement learning for individual digital twins has been tested throughout the literature, and the extension of RL scope to MAS systems by learning of collaboration takes organizational concepts into practical implementation within complex, highly constrained industrial environments. The things that we tried to concretize by the integration of several independent domain-based agent types and instances that create a cooperative behavior in order to achieve a set of defined goals and plans taking advantage of individual agents learning experience feedback offline environment states and rewards, and agents’ networks communicated observations and analyzed information. The proposed approach was tested using an application use case that highlighted its efficiency for complex monitoring tasks within safety-constrained systems. The proposed Policy model for the presented architecture helps to establish a conceptual framework for the behavioral mapping of actions and operations during runtime mode, but also for offline learning tasks in which results are transmitted for online execution. The suggested implementation framework based on the simulation of DT agents in a virtual environment is intended to establish a monitoring interface for this policy-based verification mechanism for agents’ complex and unpredictable network behaviors. In the following studies, we aim to explore the integration and runtime security verification policy aspects of the proposed architecture for highly critical industrial environments and work on the testing and simulation of the proposed architecture in real industrial environments for applications in complex systems life cycle management and implementation of DT2Aero in a real environment. Data availability Underlying data Zenodo: DT-MAS smart architecture for prescreptive maintenance of aircraft fuel distribution systems, https://doi.org/10.5281/zenodo.10649700 . 31 This project contains the following underlying data: • Run1: 5 Scenarios with Healthy and Faulty Behaviors for pump, driver, Hydraulic Pump and Tank. Simulation of Fault Type from 1 to 5. • Run2: 5 Scenarios with Healthy and Faulty Behaviors for pump, driver, Hydraulic Pump and Tank. Simulation of Fault Type from 1 to 5 with combination of some failures. • Run3: 5 Scenarios with Healthy and Faulty Behaviors for pump, driver, Hydraulic Pump and Tank. Simulation of Fault Type 6 and 7. Change of operating conditions for Healthy scanrios. Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Software availability Software available from: https://doi.org/10.5281/zenodo.10649700 . Source code available from: https://github.com/GhitaMezzourDT/DigitalTwinForAircraftMaintenance . License: This project is licensed under Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). References 1. Wolf M, Semm A, Erfurth C: Digital Transformation in Companies – Challenges and Success Factors. Springer International Publishing; 2018. Publisher Full Text 2. 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Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 19 Mar 2024 ADD YOUR COMMENT Comment Author details Author details 1 Engineering Research Laboratory, National and high school of electricity and mechanic (ENSEM) HASSAN II University, Casablanca, Casablanca, Morroco, Morocco 2 Engineering and Research, Research Foundation for Development and Innovation in Science and Engineering, Casablanca, Morocco Mezzour Ghita Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation Benhadou Siham Roles: Funding Acquisition, Investigation, Project Administration, Supervision, Validation, Writing – Review & Editing Benhadou Mariam Roles: Project Administration, Resources, Supervision, Writing – Review & Editing Haddout Abdellah Roles: Funding Acquisition, Project Administration, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 19 Mar 2024, 13:193 https://doi.org/10.12688/f1000research.144038.1 Copyright © 2024 Ghita M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Ghita M, Siham B, Mariam B and Abdellah H. Unleashing the potential of digital twins: a new era with aeronautics 4.0 [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2024, 13 :193 ( https://doi.org/10.12688/f1000research.144038.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 19 Mar 2024 Views 0 Cite How to cite this report: Bouhali I. Reviewer Report For: Unleashing the potential of digital twins: a new era with aeronautics 4.0 [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2024, 13 :193 ( https://doi.org/10.5256/f1000research.157776.r374615 ) The direct URL for this report is: https://f1000research.com/articles/13-193/v1#referee-response-374615 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 16 Apr 2025 Imane Bouhali , PRISME Laboratory, Institut National des Sciences Appliquees Centre Val de Loire, Bourges, Centre-Val de Loire, France; QUARTZ Laboratory, Institut superieur de mecanique de Paris, Saint-Ouen, Île-de-France, France Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.157776.r374615 The paper discusses the transformative role of digital twin and digital thread concepts in the aeronautics sector, aiming to improve aircraft design, manufacturing, operation, and maintenance processes, which is an interesting topic. By discussing how competitiveness across industries has led ... Continue reading READ ALL The paper discusses the transformative role of digital twin and digital thread concepts in the aeronautics sector, aiming to improve aircraft design, manufacturing, operation, and maintenance processes, which is an interesting topic. By discussing how competitiveness across industries has led to increased systems engineering complexity and a shift towards digital transformation in manufacturing, the paper introduces digital threads as part of digital engineering frameworks designed to improve manufacturing efficiency and cost management. It explores how digital twins (virtual, interactive representations of real entities) serve as unified data sources throughout the asset lifecycle, fostering improved information sharing and decision-making. The study also addresses challenges related to deploying smart, secure control architectures for enhanced integration within factories while assessing the potential advantages these technologies offer in the aeronautics domain. The paper is well organized, sections include a review of the aeronautics value chain, a proposal for the Digital Thread Twin for Smart Aeronautics (DT2Aero 4.0) framework, and a proof of concept demonstrating digital twin applications in aircraft fuel systems to underscore the benefits in maintenance and operational optimization. However, clarity issues arise with the figures presented in the paper, such as Figure 1, which is difficult to interpret due to importation problems. The PESTEL analysis used to assess factors impacting the value chain and the digital transition's positive effects across various phases is a considerable addition, revealing digital transformation's added value in aeronautics. The D2TSAero approach appears confusing, partly due to Figure 2's resolution; simplifying terminology (e.g., using "DT" instead of "JN") might aid comprehension. It involves showcasing interactions among three networks and environments in a manufacturing context. The paper also incorrectly intertwines digital twins and digital threads, which should be distinctly defined before their application in the proposed approach. Page 9 suggests the second standard referred to, might be ISO 23247, outlining digital twins' architecture in manufacturing. Section 3 presents an interesting case study on digital twins in aircraft fuel systems, accentuating their decision-making utility, particularly in maintenance (smart scheduling and control optimization). As with other figures, Figure 3 is not clear, making it hard to discern differences between DT instances and types or understand interaction flows. Terms such as DT types need clarity, DT types likely represent unique models, whereas instances serve distinct purposes (predictive anomaly detection, smart maintenance, proactive decision-making). DT model types also require further parameter clarification (V, U, delta, h...). Lastly, Section 4 shows simulation results within virtual environments, highlighting digital twins' efficacy for complex safety-constrained system monitoring. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Digital Twins, digital thread, processes digital transition. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Bouhali I. Reviewer Report For: Unleashing the potential of digital twins: a new era with aeronautics 4.0 [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2024, 13 :193 ( https://doi.org/10.5256/f1000research.157776.r374615 ) The direct URL for this report is: https://f1000research.com/articles/13-193/v1#referee-response-374615 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Renaud G. Reviewer Report For: Unleashing the potential of digital twins: a new era with aeronautics 4.0 [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2024, 13 :193 ( https://doi.org/10.5256/f1000research.157776.r290856 ) The direct URL for this report is: https://f1000research.com/articles/13-193/v1#referee-response-290856 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 19 Jul 2024 Guillaume Renaud , National Research Council Canada, Ottawa,, ON, Canada Approved VIEWS 0 https://doi.org/10.5256/f1000research.157776.r290856 First, I must say that I am not that familiar with concepts related to Industry 4.0, PESTEL analysis, AAS, Internet of Things, etc. My experience is more on the digital twinning of specific components for life cycle management. That being ... Continue reading READ ALL First, I must say that I am not that familiar with concepts related to Industry 4.0, PESTEL analysis, AAS, Internet of Things, etc. My experience is more on the digital twinning of specific components for life cycle management. That being said, I find this article a useful summary of the different aspects related to the digital transformation in the aerospace industry. Further, it proposed a detailed DT framework, along with a demonstration of the use of some of its aspects (training) for a concrete system problem. The paper is very well written but reads as a condensed version of what could be a much larger document. A lot of information is provided; it uses many terms and acronyms that are not defined. Defining these terms would help the non-expert readers.. One major negative aspect: the text in the figures is extremely difficult and sometimes impossible to read. The image conversion should be redone. Section 2 discusses the aeronautics value chain, divided in upstream, core and downstream activities, and defines four main ecosystems: engineering, electrics and wiring, assembly and maintenance/repair. These ecosystems are analyzed to identify added value and improvement areas related to the transition to digital threads/twins. A lot of information is packed in four tables and the key observations do not really stand out. The proposed very comprehensive digital thread/twin framework, D2TSAero, consists of three networks mapped to the aforementioned value chain activities. A lot of details are addressed, including aspects such as contracting, security, communication protocols, certification, etc. I am not in position to judge if anything is missing or how novel this specific architecture is. Section 3 is a proof of concept based on the simulation of an aircraft fuel distribution system, with the intend to use DT to define smart maintenance scheduling. For the paper, simulated environment, combining healthy scenarios, noise and conflicting situations, augmented with a focus on security threats in order to integrate safety and security concerns. Pump model: some parameters are not defined (Tp, ki, V, U, delta, x, etc.). Last sentence should be rewritten (The activation of the…). Tank/valve model: some parameters are not defined. DT network of type and instance collaborate to determine smart maintenance planning using online and offline learning – defined by agent states within the environment, actions, transition probabilities and rewards. More specifically: Agent states within the environment: Highly critical assets (states: new, deteriorating, deteriorating critical, under PM, available to adjust, failed) Asset health index (deterioration and health evolution + their impact) Failure evolution (rate) Actions No maintenance, check and adjust (minor maintenance), preventive maintenance, corrective maintenance, replacement Algorithm 1: Anomaly prediction and field interrogation Algorithm 2: Smart maintenance planning Algorithm 3: Proactive decision making The objective is to determine the best maintenance actions based on costs (maintaining operations, maintenance, etc.). Section 4 present simulation results for healthy and faulty scenarios. Focus was put on normal and abnormal behavior of the system to work as an interface with the real twin. Healthy and faulty scenario data are used to train system state/health classification. Unfortunately, no demonstration using physical twin data is presented but the authors mention that it would be done in the future.. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Aircraft structures I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Renaud G. Reviewer Report For: Unleashing the potential of digital twins: a new era with aeronautics 4.0 [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2024, 13 :193 ( https://doi.org/10.5256/f1000research.157776.r290856 ) The direct URL for this report is: https://f1000research.com/articles/13-193/v1#referee-response-290856 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 19 Mar 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 19 Mar 24 read read Guillaume Renaud , National Research Council Canada, Ottawa,, Canada Imane Bouhali , Institut National des Sciences Appliquees Centre Val de Loire, Bourges, France; Institut superieur de mecanique de Paris, Saint-Ouen, France Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Bouhali I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 Apr 2025 | for Version 1 Imane Bouhali , PRISME Laboratory, Institut National des Sciences Appliquees Centre Val de Loire, Bourges, Centre-Val de Loire, France; QUARTZ Laboratory, Institut superieur de mecanique de Paris, Saint-Ouen, Île-de-France, France 0 Views copyright © 2025 Bouhali I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The paper discusses the transformative role of digital twin and digital thread concepts in the aeronautics sector, aiming to improve aircraft design, manufacturing, operation, and maintenance processes, which is an interesting topic. By discussing how competitiveness across industries has led to increased systems engineering complexity and a shift towards digital transformation in manufacturing, the paper introduces digital threads as part of digital engineering frameworks designed to improve manufacturing efficiency and cost management. It explores how digital twins (virtual, interactive representations of real entities) serve as unified data sources throughout the asset lifecycle, fostering improved information sharing and decision-making. The study also addresses challenges related to deploying smart, secure control architectures for enhanced integration within factories while assessing the potential advantages these technologies offer in the aeronautics domain. The paper is well organized, sections include a review of the aeronautics value chain, a proposal for the Digital Thread Twin for Smart Aeronautics (DT2Aero 4.0) framework, and a proof of concept demonstrating digital twin applications in aircraft fuel systems to underscore the benefits in maintenance and operational optimization. However, clarity issues arise with the figures presented in the paper, such as Figure 1, which is difficult to interpret due to importation problems. The PESTEL analysis used to assess factors impacting the value chain and the digital transition's positive effects across various phases is a considerable addition, revealing digital transformation's added value in aeronautics. The D2TSAero approach appears confusing, partly due to Figure 2's resolution; simplifying terminology (e.g., using "DT" instead of "JN") might aid comprehension. It involves showcasing interactions among three networks and environments in a manufacturing context. The paper also incorrectly intertwines digital twins and digital threads, which should be distinctly defined before their application in the proposed approach. Page 9 suggests the second standard referred to, might be ISO 23247, outlining digital twins' architecture in manufacturing. Section 3 presents an interesting case study on digital twins in aircraft fuel systems, accentuating their decision-making utility, particularly in maintenance (smart scheduling and control optimization). As with other figures, Figure 3 is not clear, making it hard to discern differences between DT instances and types or understand interaction flows. Terms such as DT types need clarity, DT types likely represent unique models, whereas instances serve distinct purposes (predictive anomaly detection, smart maintenance, proactive decision-making). DT model types also require further parameter clarification (V, U, delta, h...). Lastly, Section 4 shows simulation results within virtual environments, highlighting digital twins' efficacy for complex safety-constrained system monitoring. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Digital Twins, digital thread, processes digital transition. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Bouhali I. Peer Review Report For: Unleashing the potential of digital twins: a new era with aeronautics 4.0 [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2024, 13 :193 ( https://doi.org/10.5256/f1000research.157776.r374615) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-193/v1#referee-response-374615 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Renaud G. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 19 Jul 2024 | for Version 1 Guillaume Renaud , National Research Council Canada, Ottawa,, ON, Canada 0 Views copyright © 2024 Renaud G. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions First, I must say that I am not that familiar with concepts related to Industry 4.0, PESTEL analysis, AAS, Internet of Things, etc. My experience is more on the digital twinning of specific components for life cycle management. That being said, I find this article a useful summary of the different aspects related to the digital transformation in the aerospace industry. Further, it proposed a detailed DT framework, along with a demonstration of the use of some of its aspects (training) for a concrete system problem. The paper is very well written but reads as a condensed version of what could be a much larger document. A lot of information is provided; it uses many terms and acronyms that are not defined. Defining these terms would help the non-expert readers.. One major negative aspect: the text in the figures is extremely difficult and sometimes impossible to read. The image conversion should be redone. Section 2 discusses the aeronautics value chain, divided in upstream, core and downstream activities, and defines four main ecosystems: engineering, electrics and wiring, assembly and maintenance/repair. These ecosystems are analyzed to identify added value and improvement areas related to the transition to digital threads/twins. A lot of information is packed in four tables and the key observations do not really stand out. The proposed very comprehensive digital thread/twin framework, D2TSAero, consists of three networks mapped to the aforementioned value chain activities. A lot of details are addressed, including aspects such as contracting, security, communication protocols, certification, etc. I am not in position to judge if anything is missing or how novel this specific architecture is. Section 3 is a proof of concept based on the simulation of an aircraft fuel distribution system, with the intend to use DT to define smart maintenance scheduling. For the paper, simulated environment, combining healthy scenarios, noise and conflicting situations, augmented with a focus on security threats in order to integrate safety and security concerns. Pump model: some parameters are not defined (Tp, ki, V, U, delta, x, etc.). Last sentence should be rewritten (The activation of the…). Tank/valve model: some parameters are not defined. DT network of type and instance collaborate to determine smart maintenance planning using online and offline learning – defined by agent states within the environment, actions, transition probabilities and rewards. More specifically: Agent states within the environment: Highly critical assets (states: new, deteriorating, deteriorating critical, under PM, available to adjust, failed) Asset health index (deterioration and health evolution + their impact) Failure evolution (rate) Actions No maintenance, check and adjust (minor maintenance), preventive maintenance, corrective maintenance, replacement Algorithm 1: Anomaly prediction and field interrogation Algorithm 2: Smart maintenance planning Algorithm 3: Proactive decision making The objective is to determine the best maintenance actions based on costs (maintaining operations, maintenance, etc.). Section 4 present simulation results for healthy and faulty scenarios. Focus was put on normal and abnormal behavior of the system to work as an interface with the real twin. Healthy and faulty scenario data are used to train system state/health classification. Unfortunately, no demonstration using physical twin data is presented but the authors mention that it would be done in the future.. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Aircraft structures I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Renaud G. Peer Review Report For: Unleashing the potential of digital twins: a new era with aeronautics 4.0 [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2024, 13 :193 ( https://doi.org/10.5256/f1000research.157776.r290856) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-193/v1#referee-response-290856 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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