Browsing by Author "Lounici, Yacine"

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Bond Graph model-based fault estimation in presence of uncertainties: Application to mechatronic system
(Université de djelfa, 2018) Lounici, Yacine; Touati, Youcef; Adjerid, Smail
This paper deals with the fault estimation problem of uncertain systems using Bond graph model-based technique.The main objective is to enhance the fault estimation procedure based on the generation of the fault estimation thresh-old, in order to overcome the problem related to errors in the estimated fault. The novelty of the proposed method is the generation of the fault estimation error us-ing the fault estimation equation, which can be generated from the Bond graph model. The proposed methodology is validated via simulationsof a mechatronic system.
Bond Graph Model-Based Methods for Fault Diagnosis: A Comparative Study
(2019) Lounici, Yacine; Touati, Youcef; Adjerid, Smail
Advanced methods of fault diagnosis become increasinglysignificant for improving the safety, reliability and efficiently of dynamic systems in various domains of industrial engineering. This paper reviews and comparesthree bond graph model-based methods for fault diagnosis. These methods are causality inversion method, augmented Analytical redundancy relation method, and fault estimationmethod. Thesemethods are applied toa simulation model of an electricalsystem. This latteris used to simulate the system variables in both normal and faulty situationsand to generate residuals for fault detection and isolation. The results of the case study are compared for highlighting the fault diagnosisperformanceand capability of a method over another.The result showsthat the faultestimationmethodhas a better diagnosis performance when compared to the other methods
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, Smail
This 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 methods
Diagnostic et commande tolérante aux défauts des systèmes dynamiques par bond graph
(Université M'Hamed Bougara : Faculté de Technologie, 2021) Lounici, Yacine; Adjerid, Smail( Directeur de thèse)
L'augmentation continue de la complexité des systèmes et des exigences de sécurité industrielles a conduit à un intérêt croissant pour le développement de nouveaux algorithmes de diagnostic et de commande tolérant aux défauts. Dans cette thèse, nous nous sommes penchés sur le problème de diagnostic et de commande tolérante aux défauts des systèmes incertains en utilisant l'approche basée sur un modèle Bond Graph. Ce dernier assure par ses propriétés causales et structurelles une génération automatique des relations de redondance analytiques. En effet, le résidu est comparé à ses seuils pour détecter les défauts. La comparaison entre toutes les signatures de défauts permet de prendre une décision sur l'isolation des défauts. Pour isoler les défauts qui activent le même ensemble de résidus, un résidu supplémentaire doit être calculé pour chaque défaut. Ce résidu supplémentaire est la comparaison entre deux estimations du défaut considéré obtenues à l'aide des relations de sensibilité. Cependant, en raison de la présence d'incertitudes, des erreurs peuvent se produire dans l'estimation des défauts, ce qui entraîne de fausses décisions sur l'isolation des défauts. Les nouveautés et les intérêts innovants de la méthode de diagnostic de défaut proposée sont: améliorer la procédure d'estimation de défaut basée sur la modélisation des incertitudes et la notion de bicausalité, afin de surmonter le problème lié aux erreurs dans le défaut estimé et générer de manière appropriée les seuils d'isolation en utilisant la procédure d'estimation incertaine de défaut proposée dans cette thèse afin que le défaut puisse être isolé avec succès. La deuxième partie de cette thèse présente le développement d'une nouvelle stratégie de commande active tolérante aux défauts. Pour cette tâche, un schéma basé sur un modèle Bond Graph bicausal est conçu pour générer des informations en ligne vers le contrôleur inverse sur l'estimation des défauts. Ensuite, une nouvelle approche est proposée basée sur le Bond Graph bicausal inverse sous forme de transformation linéaire fractionnelle. Les nouveautés de l'approche de commande tolérante aux défauts proposée sont: exploiter le concept de puissance du Bond Graph en alimentant la puissance générée par le défaut dans le modèle inverse et combiner de manière appropriée le modèle de Bond Graph bicausal inverse avec le contrôleur de rétroaction PI de sorte que l'approche proposée est capable de compenser les effets de défaut avec un délai très court et de stabiliser la sortie souhaitée du système. Enfin, les algorithmes proposés tout au long de ce manuscrit sont implémentés et testés. Deux applications sont étudiées. La première concerne un véhicule autonome intelligent, appelé RobuCar. La deuxième partie concerne l'application à un robot mobile omnidirectionnel, appelé Robotino
Inverse bond graph Model-Based active fault tolerant control for health monitoring of electric vehicle path tracking
(IEEE, 2020) Lounici, Yacine; Touati, Youcef; Adjerid, Smail; Touzout, Walid
This article deals with the integration of fault estimation with inverse Bond Graph model for health monitoring of an electric vehicle. This autonomous vehicle is a multiple-input multiple-output system with four electromechanical traction subsystems. The innovative interest of this work is to exploit one graphical approach not only for vehicle dynamics modeling and diagnosis but also for fault estimation and fault-tolerant control. For robust fault diagnosis, residuals are generated in the presence of uncertainties. The purpose of using fault estimation is to generate an accurate fault magnitude to the inverse bond graph system. The latter aims to compensate for the power generated by the fault. This structure is then applied to an electric vehicle in order to monitor the system in real-time and to correct the tracking in faulty situations
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, Yacine
This 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
A novel fault-tolerant control strategy based on inverse bicausal bond graph model in linear fractional transformation
(SAGE Publications, 2021) Lounici, Yacine; Touati, Youcef; Adjerid, Smail; Benazzouz, Djamel; Chebouba, Billal Nazim
This article presents the development of a novel fault-tolerant control strategy. For this task, a bicausal bond graph model-based scheme is designed to generate online information to the inverse controller about the faults estimation. Secondly, a new approach is proposed for the fault-tolerant control based on the inverse bicausal bond graph in linear fractional transformation form. However, because of the time delay for fault estimation, the PI controller is used to reduce the error before the fault is estimated. Hence, the required input that compensates the fault is the sum of the control signal delivered by the PI controller and the control signal resulting from the inverse bicausal bond graph for fast fault compensation and for maintaining the control objectives. The novelties of the proposed approach are: (1) to exploit the power concept of the bond graph by feeding the power generated by the fault in the inverse model (2) to suitably combining the inverse bicausal bond graph with the PI feedback controller so that the proposed strategy can compensate for the fault with a very short time delay and stabilize the desired output. Finally, the experimental results illustrate the efficiency of the proposed strategy
Robust fault diagnosis of hybrid systems with Interval-Valued uncertainties using hybrid bond graph
(IEEE, 2020) Lounici, Yacine; Touati, Youcef; Ould Bouamama, Belkacem; Adjerid, Smail; Chebouba, Billal Nazim
In this paper, a new robust fault diagnosis procedure for an uncertain hybrid system based on the hybrid bond graph model is proposed. The main objective is to enhance the robustness in the presence of uncertainties in order to minimize the non-detection and false alarm. The scientific interest of the present work remains in integrating the benefits of Hybrid bond graph and Interval analysis properties for effective diagnosis of uncertain hybrid systems. For this task, first, the Intervalvalued Analytical redundancy relations which may undergo discrete mode changes are derived from diagnosis hybrid bond graph with controlled junctions. Secondly, the uncertainties are modelled directly in the hybrid bond graph as interval models for interval-valued thresholds generation. The limitations of the existing methods are alleviated by the proposed method. The effectiveness of the proposed method is demonstrated through simulation on a controlled two-tank hybrid system
Robust Fault Diagnosis of SCARA Industrial Robot Manipulator
(2018) Lounici, Yacine; Touati, Youcef; Adjerid, Smail
Nowadays, robotic systems are being in increasingly demanding in many industrial activities. In order to achieve the maximal performance, complex nonlinear dynamic robotic systems were developed. However, as a consequence, the rate of component malfunctions augments with the complexity of systems. These malfunctions are called faults, which may appear in different parts of the system and can induce changes in the dynamic behaviour. This paper deals with fault diagnosis of a particular kind of industrial robots called selective compliance assembly robot arm (SCARA), where both parameter and measurement uncertainties are taken into account. Residuals and thresholds are generated using the quantitative model-based method. The inverse geometric model is used to find analytical solutions for joints angles and distances given the trajectory of the end effector. The presented geometric model is then used to derive the kinematic model. Using this kinematic model, the robot controller computes the necessary torque applied to each DC servomotor in order to move the robot from the current position to the next desired position. The proposed robust fault diagnosis scheme is then implemented for a SCARA manipulator and simulation results are presented in both normal and faulty situations.
Robust Fault Diagnosis using Uncertain Hybrid Bond Graph Model: Application to Controlled Hybrid Thermo-Fluid Process
(2019) Lounici, Yacine; Touati, Youcef; Adjerid, Smail; Benazzouz, Djamel
The continuous increase in engineering systems complexity and industrial safety requirements has led to a rising interest in the development of new Fault diagnosis algorithms. This paper addresses the fault diagnosis problem of uncertain hybrid systems containing both discrete and continuous modes using a hybrid bond graph (HBG) approach. The latter provides through its properties, an automatic Global Analytical Redundancy Relations (GARRs) generation. The numerical evaluation of GARRs yields fault indicators named residuals, which are used to verify the coherence between the real system behavior and reference behavior for real-time diagnosis. In fact, the residual is compared to its adaptive thresholds to detect the actual faults. In addition, the Global Fault Signature matrix (GFSM) allows making a decision on fault isolation. The main scientific interest of the proposed method remains in integrating the benefits of the HBG with the approach for adaptive thresholds generation for systems having uncertain parameters and measurements. For this task, first, the HBG model is obtained to model the hybrid system using the controlled junctions taken into consideration discrete modes changes. Secondly, the parameter and measurement uncertainties are modelled directly on the HBG in preferred derivative causality for residuals and adaptive thresholds generation. The proposed methodology is studied under various scenarios via simulation over a controlled hybrid thermo-fluid two-tank system.
Uncertain bicausal bond graph and adaptive fuzzy PID controller for Fault-Tolerant control
(IEEE, 2020) Lounici, Yacine; Touati, Youcef; Adjerid, Smail; Ould Bouamama, Belkacem; Benazzouz, Djamel
In this paper, a new fault-tolerant control scheme for uncertain systems is proposed based on uncertain bicausal Bond Graph and Adaptive Fuzzy PID Controller. The main objective is to make the control system act faster and more effectively in the presence of the faults and uncertainties. The scientific interest of the present work remains in integrating the benefits of uncertain bicausal bond graph model-based fault estimation and Fuzzy PID controller for effective fault-tolerant control of uncertain dynamic systems. For this task, the uncertain fault estimation is generated from the uncertain bicausal bond graph with perfectly separate nominal part from the uncertain part. Second, a fuzzy inference scheme is proposed to tune the PID gains in real-time, where the estimated fault and the errors over the estimated fault due to the presence of uncertainties are used in the fuzzy inference as inputs. Finally, the required input that compensates the fault effects are delivered to the system. The proposed approach is compared with the conventional one through an experimental application to the traction system of an omnidirectional mobile robot. The results show the effectiveness of the proposed approach
Uncertain fault estimation using bicausal bond graph : application to intelligent autonomous vehicle
(SAGE, 2020) Lounici, Yacine; Touati, Youcef; Adjerid, Smail
This 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 RobuCar