Digital Twin (DT) technology has recently gained significant attention in both industry and academia. By improving efficiency, reducing costs, and enabling predictive maintenance, DT plays a crucial role in control, diagnostics, prediction, and optimization. However, at the definition level, the rapid growth of research has led to a variety of definitions and inconsistent terminology, underscoring the need for a clearer and more unified understanding of the DT concept. At the design stage, two major challenges remain. The first is to develop models that are both accurate and dynamic, capable of supporting optimization and real-time supervision, including control and diagnosis. The second lies in ensuring a reliable link to the physical system through online identification, which is particularly challenging due to the non-stationary nature of real-world processes. This study addresses the definition level and the first challenge at the design level by offering a comprehensive overview of the DT concept, including its definition, development phases, and an illustration of its design carried out in a two-tank system. The research consists of using the Bond Graph (BG) tool as a multi-physical graphical tool for designing a DT not only for dynamic modeling but also for online supervision, including diagnosis and recovery decisions. The results first show the effectiveness of BG in constructing the DT using 20Sim software for dynamic modeling and fault detection and isolation. This BG tool enables DT to monitor the state of the system and provides the health indicators necessary to reconfigure the system controller.

Railway switch machines are important elements in a railway network, allowing trains to change directions. Any failure in such safety critical equipment can induce economic losses caused by traffic interruptions and even sometimes catastrophic accidents. Hence, the diagnosis of such failures is recognized to be a primordial task. In the consulted literature, the methods considered are those based on an analytical model or those based on data (artificial intelligence) or hybrid with suitable justifications. The present paper deals with synthesis and discussion of two approaches applied to railway switch machine: Linear Fractional Transformation (LFT) Bond Graph as multiphysic model based robust diagnosis and a data-driven method based on k nearest neighbor (kNN) algorithm. A simulation platform is developed to test those two algorithms under various scenarios of faults that may affect the switch machine. Results show that the LFT Bond Graph model based diagnosis whose performances depend mainly on model accuracy is robust with respect to parameters uncertainties and able to detect and isolate the majority of faults, including sensor, plant and actuator faults, while for classification-based diagnosis, even if a good accuracy is achieved with any need of complex equations, a great amount of computational time is devoted to parameters tuning.

Supervision systems include a set of tools and methods for the control of industrial processes in normal working conditions as well as in the presence of failures. The main activities addressed by supervision systems are fault detection and Isolation (FDI), and decision making for fault accommodation or system reconfiguration. The system modelling is an important and difficult step in FDI design. Main issues of supervision systems design before industrial realization, can be summarized mainly as follows: how to generate automatically a formal dynamic model and fault indicators of complex industrial continuous and hybrid systems? How to perform structural detectability and isolability performances without any need of numerical calculation before industrial design, and how to propose the optimal instrumentation architecture? Such are the questions we want to contribute from and discuss during this keynote from an industrial and theoretical point of view.

A system of systems (SoS) is a large-scale integrated system. In an SoS, many complex independent systems work collectively for a common mission. Modeling of SoS is challenging due to their large-scale and complexity of the constituent systems. In this paper, generic modeling of a class of SoS, namely mechatronic systems, is proposed based on bond graph modeling approach. Bond graph modeling enables to develop a unified model of SoS, which combines the behavioral and the organizational modeling approaches of SoS. The proposed approach for SoS modeling is applied to intelligent transportation system, where road traffic dynamic in a platoon of intelligent autonomous vehicles (IAVs) is considered for modeling. Finally, the developed bond graph model is simulated for a platoon of four IAVs forming SoS.

Hyper-graph is considered as an appropriate tool for expressing the organizational modelling of System of Systems (SoS) at macroscopic level, but not for dynamical behavior of the physical system, i.e. the microscopic level. In the other hand, the bond-graph is an excellent model dealing with microscopic level, it is however not suited for the higher levels where there is no power exchange. So, for the purpose of unifying the graphical methodologies describing System SoS abstraction levels, Some transformation rules, which consist in converting the bond-graph to a hyper-graph microscopic model are presented in this paper. Finally, The proposed transformation rules are illustrated through a pedagogical example taken from industrial application.

This paper concentrates on the development of robust maximum power control for variable speed wind turbines with a double feed induction generator in the presence of wind disturbance and parameter uncertainties. To increment the actual manner for the wind turbine system, the effects of the mechanical and electrical part are taken in consideration. The algorithm is based on Takagi-Sugeno fuzzy model to increases the efficiency of the wind power generation systems and guarantees almost convergence. Conditions for the stability of fuzzy control systems are proved using a fuzzy Lyapunov function and formulated by Linear Matrix Inequalities. A Parallel Distributed Compensation scheme of fuzzy controllers is stated for closing the feedback loop. The advantages of the proposed strategy are: i) Enough stability conditions in the LMI is given for uncertain nonlinear system; ii) stabilizes the system over a wide range of wind speed variations and parameter uncertainties; iii) maximize output power from the system; iv) improving the system efficiency than other techniques. The influence of the proposed controller is finally proved through numerical simulations and compared with different strategies.

Direct braking system is a key part of train security. This system is complex and nonlinear because of coupling multi-physic energies (pneumatic, electric, mechanical translation and rotation) and working in hybrid structure involving continuous and discrete modes because of presence of relays and diodes. The objective of the present paper is to use one representation: a hybrid bond graph as a multidisciplinary and integrated tool for dynamic modelling of all modes but also structural diagnosability analysis to improve the preventive maintenance, to insure the reliability and availability of the train for its health management.

Being a very efficient and clean energy converter, a proton exchange membrane fuel cell may be utilized to power an electrical vehicle efficiently. Nevertheless, degradation mechanisms affect the lifespan of this electrochemical converter. Consequently, the estimation of the State of Health and Remaining Useful Life have been the subject of numerous researches in the past years. However, most of the methods available considering fuel cell prognostic do not allow the uncertainty quantification of the estimation that can be implemented online considering the calculation cost. As a novelty, the present article depicts a prognostic algorithm based on an Extended Kalman Filter. This observer estimates the State of Health, the speed of the degradation and also provides the estimation uncertainty. Then, an Inverse First Order Reliability Method computes the Remaining Useful Life with a 90% confidence interval based on the estimation of the observer. This method is applied on a 175 hours data set subsequent of an experiment on an 8-cells fuel cell stack that was subjected to an automotive power profile.

Fuel cells, especially the proton exchange membrane fuel cell, are promising energy converters that can be used for various applications (transportation, mobile and stationary applications). Although, they have a limited lifespan due to degradation mechanisms (chemical and mechanical stresses) that are not completely understood. Consequently, researches have been conducted to estimate the Remaining Useful Life of such devices, in order to take mitigation actions, extending the life of this electrochemical converter. The aim of the presented work is to propose an observer based method for prognostic of Proton Exchange Membrane Fuel Cell. An Extended Kalman Filter estimates the State of Health and its derivative using a single empirical degradation model, in any operating conditions. Those estimations are used to predict the future State of Health. Once the prediction equalizes a defined threshold, the fuel cell is considered out of use and the Remaining Useful Life is given. This method is validated by simulation and then used on a set of experimental data resulting from a long term test on a 5-cell stack operated under a constant current solicitation.

The present paper deals with bond graph model-based for structural component fault detection and isolation. The structural conditions of fault detectability and isolability are obtained directly from the bond graph using the properties of the bicausality and the causal path. It is shown that the monitorability analysis using bond graph is automatically deduced using this unified tool, with respect to the detectability and isolability conditions. A real mechatronic system application of intelligent autonomous vehicle is given to show the efficiency and the simplicity analysis of the proposed approach. This paper was motivated by the problem of integrated design of a fault diagnosis system by considering both, system instrumentation and the set of specifications regarding faults. Existing methods dealing with such problems are based mainly on the existing system instrumentation. In this paper, a fault diagnosis system study and analysis is proposed. This is done by using a unified graphical tool such as Bond Graph tool which is used for system modeling, structural analysis and fault diagnosis conclusions. Therefore, system modeling, fault monitorability analysis, and fault indicator generation are all performed by using the same graphical tool. In addition, the proposed method may be exploited for monitorability analysis before industrial design, i.e., ability to detect and isolate faults with given instrumentation architecture and how to make faulty components monitorable by adding new sensors. To show the effectiveness of the proposed method, an application on real mechatronic system is considered.

Currently traffic management is becoming more important to achieve the goal of sustainable transport, and a good traffic model can describe the traffic behavior efficiently. The traffic models can be classified based on level of details as submicroscopic-, microscopic-, mesoscopic-, and macroscopic-level models. In this paper, we provide a review of the four types of models (submicro, micro, meso, and macro) and then propose a multilevel model of traffic, which combines submicroscopic, microscopic, and macroscopic levels of traffic model. In this work, we do not consider the mesoscopic-level model. At the submicroscopic level, we develop a bond graph model of a four-wheeled vehicle considering the longitudinal, lateral, yaw, and actuator dynamics. At the microscopic level, we develop a car-following model based on virtual interconnections between the submicroscopic bond graph models of vehicles. Then, at the macroscopic level, we deduce macroscopic variables (average speed, density, and flow) from the submicroscopic and microscopic models. Having a multilevel model of traffic allows combining two properties of modeling simulation, one in real-time mode at microscopic and submicroscopic levels and the other at offline mode at macroscopic level. Thus, the whole supervision of the road traffic can be performed. Finally, the multilevel model of traffic is validated on a real-time simulator of vehicle dynamics, based on experimental measurements acquired from intelligent autonomous vehicles (IAVs). In addition, real experiments on IAVs are performed to validate the model.

The graphical approaches often have different backgrounds and view a system or an algebraic model from different perspectives in order to facilitate the communication and the understanding. These graphical approaches satisfy the modeling needs and give a clear and easily understandable overview of the behavioral and functional models and make easier to see what the process is, which vulnerabilities and asset that are involved and how the system works. The main goal of this paper is to develop and implement a methodology which combines the functional analysis and the bond graph (BG) tool for intelligent and autonomous systems. As a result, a supervisory interface is obtained, given under a finite automaton, displaying to the operators the possibilities the system has to achieve or not, its objectives. Each operating mode, corresponding to a vertex of the automaton, is associated with a set of services from a functional point-of-view and is defined accurately by a behavioral BG model. Furthermore, the service availability (associated to the BG elements) and the conditions for switching from one mode to another one are analyzed by fault detection and isolation algorithms generated on the basis of the structural and causal properties of the BG tool. Moreover, when a fault is not completely isolable some results can nevertheless be expressed in terms of available or unavailable services.

This paper deals with model-based supervision of a class of system of systems (SoS). The SoS is modeled using hypergraphs, where their architectural representation allows the application of a supervision strategy, from ascending or descending directions directly on the graph. From ascending direction, it is possible to detect local or global faults on a system, when some constraints are no longer satisfied. This is done using the concept of “degree of satisfaction,” where the new operating mode of SoS is deduced using the principle of automata. In the descending direction and based on the constraint satisfaction problem, we can check from the hypergraphs a possible structural reconfiguration of SoS in the presence of faults. This reconfiguration is possible when the available systems satisfy a maximum number of constraints. Finally, an application of a SoS is presented. This is done using intelligent autonomous vehicles in a platoon configuration within a seaport terminal.

A supervision system oversees the operating state of an autonomous vehicle through the availability of the functions and services provided by the vehicle components. However, functional representations do not take into account the system dynamic behaviour and suffer from subjective definitions. For this reason, Bond Graph Models, through their behavioural, structural and causal properties are used to overcome the limitations of functional models. We obtain a new tool taking the form of a finite automaton, to provide fault identification and the reconfiguration conditions of a system. Each operating mode, corresponding to a vertex of the automaton, is associated with a set of services from a functional point of view and is defined accurately by a behavioral bond graph model. Furthermore, the service availability (associated to the Bond Graph elements) and the conditions for passage from one mode to another are analysed by fault detection and isolation algorithms generated on the basis of the structural and causal properties of the bond graph tool. The proposed approach is illustrated by a traction system of an intelligent and autonomous vehicle.

Tire-road is heterogeneous, complex and variable system due to the interaction of several physical phenomenon (mechanical, thermal, hydrodynamic,...). Modelling of such system needs a unified approach using one representation to provide in generic and systematic way the dynamic nonlinear model. The bond graph tool is well suited for this task allowing to estimate the longitudinal impact efforts through the tire-road contact, and to represent overall deformation of the tire in interaction with its environment relating to the longitudinal slip velocity. A practical tool for simulation in modular form is developed under appropriate software allowing the primary validation of the model. Finally, a comparison with experimental results is presented