Publications Scientifiques
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Item A comparison of bond graph Model-Based methods for fault diagnosis in the presence of uncertainties : application to mechatronic system(IEEE, 2019) Lounici, Yacine; Touati, Youcef; Adjerid, SmailThis paper deals with comparing three methods for robust fault diagnosis that generate their residuals using bond graph model. These methods are the causality inversion method, a sensor data combinations method, and a faults/residuals sensitivity relations method. In addition, both parameter and measurement uncertainties are considered to generate the adaptive residual thresholds. Through simulation on a mechatronic system, the presented methods are studied under sensor and parameter faults. The results of the case study are compared for gaining practical insights about the applicability and performance of these methods. The results show that the faults/residuals sensitivity relations method has a better diagnosis performance as compared to the other methodsItem Uncertain fault estimation using bicausal bond graph : application to intelligent autonomous vehicle(SAGE, 2020) Lounici, Yacine; Touati, Youcef; Adjerid, SmailThis article addresses the fault detection and isolation problem of uncertain systems using the bond graph model–based approach. The latter provides through its causal and structural properties an automatic analytical redundancy relations generation. The numerical evaluation of analytical redundancy relations yields residuals, which are used to verify the coherence between the real system and reference behaviors describing the nominal operation. In fact, the residual is compared to its thresholds to detect the fault. In addition, the comparison between all fault signatures allows making a decision on fault isolation. Moreover, to isolate the faults that activate the same set of residuals, an additional residual must be calculated for each fault. This additional residual is the comparison between two estimations of the considered fault obtained using the sensitivity relations. However, due to the presence of uncertainties, errors can occur in the fault estimation allowing false decisions on fault isolation. The novelties and innovative interests in the present work are (1) to improve the fault estimation procedure based on the uncertainties modeling and bicausality notion, in order to overcome the problem related to errors in the estimated fault and (2) to suitably generate the isolation thresholds in a systematic way using the uncertain fault estimation procedure proposed in this article so that fault can be isolated successfully. The proposed methodology is studied under various scenarios via simulations over an electromechanical traction system corresponding to a quarter of intelligent autonomous vehicle, named RobuCarItem Multi-objective system reliability-redundancy allocation in a power plant by considering three targets(IEEE, 2020) Chebouba, Billal Nazim; Mellal, Mohamed Arezki; Adjerid, Smail; Lounici, YacineThis paper addresses the overall system reliability-redundancy allocation problem (RRAP) of an overspeed protection system in a power plant. Generally, this type of optimization problem is considered as a single objective optimization problem subject to a set of nonlinear constraints. In the present work, the optimization problem is solved with a multi-objective approach rather than a single-objective one with three conflicting objective functions, namely the reliability, cost, and volume. A fast and elitist multi-objective genetic algorithm (NSGA-II) is implemented to identify the optimal solutions for the decision-maker