Kamel Benothman

State and sensor faults estimation via a proportional integral observer

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.

Adaptive observer for fault estimation in nonlinear systems described by a Takagi-Sugeno model

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.

An approach of faults estimation in Takagi-Sugeno fuzzy systems

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.

A new method for determining PCA models for system diagnosis

In this paper, a new method is proposed to determine the structure of PCA models for system diagnosis. This method based on the principle of variable reconstruction determines PCA models in order to optimize detection and isolation of simple and multiple faults affecting redundant or non redundant variables. This new method has been validated by a simulation example of a nonlinear system.

Nonlinear progressive accommodation of actuator fault with optimal control

In this paper, a method of actuator fault tolerant control for a class of nonlinear systems is proposed. It concerns the problem of nonlinear progressive accommodation to actuator failure. This strategy is based on the optimal nonlinear controller which obtained by solving the Generalized Hamilton-JacobiBellman Equation, and its objective to maintain the system closed loop stability when an actuator fault appear. An example is given to illustrate this approach.

Failures analysis of systems modeled by Mixed Fault Trees

This paper presents a comparative study of availability evaluation of systems modeled by a mixed fault tree (MFT). This MFT contain a static sub-trees and dynamic sub-trees. These sub-trees can be independent or dependent. To evaluate the availability of a system from its fault tree, three methods are applied here: the first uses the classic Bayesian networks (CBN) for static sub-trees and dynamic Bayesian networks (DBN) for dynamic sub-trees, the second being proposed, bases on the binary decision diagrams (BDD) for static sub-trees and dynamic Bayesian networks (DBN) for dynamic sub-trees and the third being proposed uses the binary decision diagram (BDD) for static sub-trees and Markov chains (MC) for dynamic sub-trees . The three methods are compared in both cases; independence and dependence of sub-trees.