Publications Scientifiques

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Author: search.filters.author.Adjerid, SmailSubject: search.filters.subject.Diagnosis

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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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    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
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    Fault detection and isolation based on neural networks case study : steam turbine
    (2011) Benazzouz, D.; Benammar, Samir; Adjerid, Smail
    The real-time fault diagnosis system is very important for steam turbine generator set due serious fault re-sults in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using Levenberg-Marquardt algorithm related to tuning parameters of Artificial Neural Network (ANN). The model of novel fault diagnosis system by using ANN are built and analyzed. Cases of the diag-nosis are simulated. The results show that the real-time fault diagnosis system is of high accuracy and quick convergence. It is also found that this model is feasible in real-time fault diagnosis. The steam turbine is used as a power generator by SONELGAZ, an Algerian company located at Cap Djinet town in Boumerdes dis-trict. We used this turbine as our main target for the purpose of this analysis. After deep investigation, while keeping our focus on the most sensitive parts within the turbine, the weakest and the strongest points of the system were identified. Those are the points mostly adequate for failure simulations and at which the de-signed system will be better positioned for irregularities detection during the production process