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Kamel Benothman

Bio: Kamel Benothman is an academic researcher from École Normale Supérieure. The author has contributed to research in topics: Fault (power engineering) & Nonlinear system. The author has an hindex of 7, co-authored 18 publications receiving 167 citations.

Papers
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Journal ArticleDOI
TL;DR: A holistic approach to model the Safety Instrumented Systems (SIS) based on Switching Markov Chain and integrates several parameters like Common Cause Failure, Imperfect Proof testing, partial proof testing, etc.

37 citations

Proceedings ArticleDOI
23 Mar 2009
TL;DR: In this article, a proportionnal integral observer with unknown inputs is used to reconstruct state and sensors faults in noisy systems, and a mathematical transformation is made to conceive an augmented system, in which the initial sensor fault appears as an unknown input.
Abstract: This paper deals with the problem of fault detection and identification in noisy systems. A proportionnal integral observer with unknown inputs is used to reconstruct state and sensors faults. A mathematical transformation is made to conceive an augmented system, in which the initial sensor fault appear as an unknown input. The noise effect on the state and fault estimation errors is also minimized. The obtained results are then extended to nonlinear systems described by nonlinear Takagi-Sugeno models.

36 citations

Journal ArticleDOI
TL;DR: In this article, a new method for determining the principal component analysis (PCA) model structure for system diagnosis is proposed, based on the variables reconstruction principle, which determines the PCA model optimizing detection and isolation of single or multiple faults affecting redundant or non redundant variables of a system.
Abstract: In this paper, a new method for determining the Principal Component Analysis (PCA) model structure for system diagnosis is proposed. This method, based on the variables reconstruction principle, determines the PCA model optimizing detection and isolation of single or multiple faults affecting redundant or non redundant variables of a system. This new method has been validated by a simulation example.

23 citations

Proceedings ArticleDOI
23 Jun 2010
TL;DR: In this paper, an adaptive proportional integral observer is designed to estimate both the system state and sensor and actuator faults which can affect the system, and the model of the system is first augmented in such a manner that the original sensor faults appear as actuator fault in this new model.
Abstract: This paper deals with the problem of fault estimation for linear and nonlinear systems. An adaptive proportional integral observer is designed to estimate both the system state and sensor and actuator faults which can affect the system. The model of the system is first augmented in such a manner that the original sensor faults appear as actuator faults in this new model. The faults are then considered as unknown inputs and are estimated using a classical proportional-integral observer. The proposed method is first developed for linear systems and is then extended to nonlinear ones that can be represented by a Takagi-Sugeno model. In the two cases, examples of low dimensions illustrate the effectiveness of the proposed method.

20 citations

Proceedings ArticleDOI
16 May 2010
TL;DR: In this work, the problem of fault detection and identification in systems described by Takagi-Sugeno fuzzy systems is studied and a proportional integral observer is conceived in order to reconstruct state and faults which can affect the system.
Abstract: In this work, the problem of fault detection and identification in systems described by Takagi-Sugeno fuzzy systems is studied. A proportional integral observer is conceived in order to reconstruct state and faults which can affect the system. In order to estimate actuator and sensor faults, a mathematical transformation is made to conceive an augmented system, in which the initial sensor fault appears as an actuator fault. Considering actuator fault as an unknown input, one can use an unknown inputs estimation method. The noise effect on the state and fault estimation is also minimized.

13 citations


Cited by
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Journal ArticleDOI
A.M. Nagy Kiss1, Benoît Marx1, Gilles Mourot1, Georges Schutz, José Ragot1 
TL;DR: In this paper, the observer synthesis for uncertain nonlinear systems and affected by unknown inputs, represented under the multiple model (MM) formulation with unmeasurable premise variables, is considered.

66 citations

Journal ArticleDOI
TL;DR: The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer.
Abstract: The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks.

53 citations

Journal ArticleDOI
TL;DR: The goal of this paper is to study the statistical performances of the constrained generalized likelihood ratio test used to detect an additive anomaly in the case of bounded nuisance parameters.

47 citations

Journal ArticleDOI
TL;DR: In this paper, a modified principal component analysis (PCA) algorithm was proposed to improve the signal-to-noise ratio (SNR) in inchoate faulty signals, in which the optimal subspace is selected via a cumulative percent of variance (CPV) criterion and the test statistic condition of the true information loss.
Abstract: This paper addresses the development of an algorithm that can improve the signal-to-noise ratio (SNR) in inchoate faulty signals. The removal of noise and preservation of fault information components cannot be easily achieved. Many techniques for SNR improvement in healthy signals rely on frequency bands. Such techniques have been proven to be efficient in improving the SRN by filtering out frequency bands (FoFBs). However, these techniques cannot reduce noise and preserve fault information when dealing with inchoate faulty signals. Thus, a feature extraction technique based on statistical parameters, which are free from Gaussian noise, is proposed in this paper. The proposed signal subspace-based approach for SNR improvement in inchoate faulty signals is based on a modified principal component analysis (PCA), in which the optimal subspace is selected via a cumulative percent of variance (CPV) criterion and the test statistic condition of the true information loss, which has the tendency to alleviate the impact of Gaussian and non-Gaussian noise and provides useful time domain analysis for non-stationary signals such as vibration, in which spectral contents vary with respect to time. Furthermore, the modified PCA algorithm is combined with a low-pass filter (LPF) to achieve an optimum balance between noise reduction efficiency and the conservation of inchoate fault information. The proposed PCA-LPF algorithm is compared with different filters under different noise levels to find the most efficient approach in terms of optimizing the trade-off between noise reduction efficiency and precision of inchoate fault information conservation, with the final goal of improving the fault detection capability. Further, the performance of the proposed PCA-LPF algorithm was demonstrated with an experimental study on vibration-based ball bearing fault detection.

45 citations

Journal ArticleDOI
TL;DR: The stability of the proposed FD and prognostics scheme is verified using the Lyapunov theory and two different simulation case studies are considered to verify the theoretical conjectures presented in this paper.
Abstract: In this paper, a novel model-based fault detection (FD) and prediction scheme is developed for a class of Takagi-Sugeno (T-S) fuzzy systems. Unlike other FD schemes, in the proposed design, an FD observer with online fault learning capability is utilized to generate a residual which is obtained by comparing the system output with respect to the observer output. A fault is declared active if the generated residual exceeds an a priori chosen threshold. Subsequently, the fault magnitude is estimated online by using a suitable parameter update law. Upon detection, the online estimate of the fault magnitude is used in a mathematical equation to determine time-to-failure (TTF) or remaining useful life. TTF is determined by projecting the estimated fault magnitude at the current time instant against a failure threshold. Note that the previously reported FD schemes could neither estimate the magnitude of a growing fault in real time nor were they able to predict the remaining useful life of the fuzzy system. In this paper, the stability of the proposed FD and prognostics scheme is verified using the Lyapunov theory. Finally, two different simulation case studies are considered to verify the theoretical conjectures presented in this paper.

43 citations