Publications Scientifiques

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Author: search.filters.author.Adjerid, Smail

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    Vieillissement et Obsolescence des équipements industriels : Traitement Fiabiliste et Management de la Maintenance
    (IEEE, 2021) Chabane, Ali; Adjerid, Smail; Alem, Said; Aggad, maya
    Cet article considère le problème du vieillissement et de l’obsolescence dans le management de la maintenance. La stratégie de maintenance a des répercussions directes sur l’exploitation d’un système, la production et les charges financières. L’objectif de ce travail est d’apporter une contribution à l’analyse de l’obsolescence et du vieillissement des composants d’un véhicule industriel utilisés par la Société COSIDER. Les résultats de ce travail montrent les vieillissements rapides qui apparaissent pendant le cycle de vie du véhicule, et montrent l’importance du choix des équipements et des pièces de rechange pour réduire le taux de défiance. Mots-clés : maintenance industrielle, Stratégie de la maintenance, Sûreté de fonctionnement, vieillissement des équipements industriels, Obsolescence des équipements industriels.
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    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.
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    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.
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    Three computational intelligence methods for system reliability
    (2018) Chebouba, Billal Nazim; Mellal, Mohamed Arezki; Adjerid, Smail
    Nowadays, the competitiveness in the industrial world became more and more harsh, which requires that the system must be as reliable as possible. In many optimization problems, hard fitness functions are considered. Those functions can- not be solved by the traditional mathematical programming methods. An alternative solution to the conventional approaches. The meta-heuristic optimization tech- niques are used, due to their ability to obtain global or near-global optimum solu- tions. In the present paper, we address the system reliability-redundancy allocation optimization problem, using three well known algorithms namely, the Cuckoo Search (CS), Particle Swarm Optimization (PSO), and the Fractal Search (SFS). The constraints defined here in the problem are handled with the help of the penalty function method. The results of the numerical case study are compared for high- lighting the superiority of an algorithm over another.
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    Multi-objective Pipeline Network Reliability Optimization by Particle Swarm Optimization
    (2019) Chebouba, Billal Nazim; Mellal, Mohamed Arezki; Adjerid, Smail
    This paper presents the use of a multi-objective optimization algorithm for solving the reliability allocation problem in case of reliability of pipeline installations in a network configuration. Usually, this kind of problem is considered as a single objective subject to one or several nonlinear constraints and solved by using either mathematical programming techniques or more recently by special metaheuristics. In the present work, the problem is considered as a multi-objective optimization problem. The MOPSO algorithm is used and demonstrates its ability to identify the set of optimal solutions (Pareto front) providing to the decision maker the optimal solution space
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    Multi-objective System Reliability Optimization in a Power Plant
    (IEEE, 2018) Chebouba, Billal Nazim; Mellal, Mohamed Arezki; Adjerid, Smail
    This paper presents the use of a multi-objective optimization algorithm for solving the reliability-redundancy allocation problem in case of an overspeed protection system of a power plant. Usually, this kind of problem is considered as a single objective subject to one or several nonlinear constraints and solved by using either mathematical programming techniques or more recently by special metaheuristics. In the present work, the problem is considered as a multi-objective optimization problem. The NSGA-II algorithm is used and demonstrates its ability to identify the set of optimal solutions (Pareto front) providing to the decision maker the optimal solution space
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    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
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    Dependability analysis in systems engineering approach using the FMECA extracted from the SysML and failure modes classification by K-means
    (Springer, 2021) Chabane, Ali; Adjerid, Smail; Meddour, Ikhlas
    The work presented in this article is a contribution to the implementation of an approach dedicated to the behavioral analysis of industrial systems starting from the design Maintenance engineering techniques inspire the suggested approach, and it aims at deducing and classifying the industrial systems' probable failure modes. The latter modes can alter the systems proper functioning. Our suggested approach is a combination of three complementary tools. The SysML language is applied to express customers' needs and requirements, such as future systems' functions and operating conditions. Besides, the FMECA method analyzes systems' potential dysfunction and the recommendation of appropriate maintenance actions. Finally, the K-means method classifies failure modes to get detailed mode criticality instead of calculating this latter according to ancient methods. The result will objectively make it possible to develop systems with reliable and maintainable components. It also helps to recommend optimal maintenance strategies according to the equipment evolution state. The approach is carried out through two application cases. The first is a practical and straightforward system used to check the methods feasibility, and the second is a more elaborated one, used to observe the effectiveness of the approach
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    Development of a New Strategy to Extract Dangerous Scenarios from Petrochemical Industry Installation
    (Springer, 2020) Aggad, Maya; Adjerid, Smail; Benazzouz, Djamel
    The use of Petri net reachability graph remains one of themost popular methods to extract critical scenarios that lead the system to a dangerous state. However, in complex systems, explosion states space and confusion between causality and precedence relationship between events are the two major limits making reachability graph inefficient to perform such analysis. In the last decade, the first limitation was tackled by an approach that uses the Petri net structure. It considers only the last normal state and ignores the rest of the network. Nevertheless, no research work appears in the literature, to consider the second limitation. In this sense, this paper proposes a novel approach based on Petri net and linear logic, to overcome the two limits. To prove the effectiveness of this proposal, the approach was applied on a petrochemical installation consisting of a cooling flammable fluids storage bins system. The obtained results are compared with the two existing approaches, the first using reachability graph and the second using the Petri net structure. The new proposed approach has shown higher performances compared to the previously mentioned methods.
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    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