Fayçal Ben Hmida

Optimal preventive maintenance strategy using two repair crews having different efficiencies: a quasi renewal process based modelling approach

This paper considers randomly failing, single-unit equipment subject to a periodic preventive maintenance (PM) policy. In case of failure between successive perfect PM actions (renewals), imperfect repairs are performed following a decreasing quasi-renewal process. One of two different maintenance crews can perform the repairs. One team is more experienced, and consequently more efficient than the other, but more costly. A mathematical model is developed in order to determine the PM period, T, and the kth repair, during a PM period, after which the repair team should be changed, minimising the average total cost per time unit over an infinite time span. It is also proved that an optimal solution in terms of the PM period always exists for any given system lifetime distribution and any set of maintenance costs. Numerical examples are presented and the obtained results are discussed.

Robust Delay-Derivative-Dependent Sliding Mode Observer for Fault Reconstruction: A Diesel Engine System Application

In this paper, a new delay-derivative-dependent sliding mode observer (SMO) design for a class of linear uncertain time-varying delay systems is presented. Based on this observer, a robust actuator fault reconstruction method is developed. In the meantime, the considered uncertainty is bounded and the time-delay is varying and affects the state system. Besides, the dynamic properties of the observer are analyzed and the reachability condition is satisfied. Applying the developed SMO, the

Bayesian estimation via extended and unscented Kalman particle filtering for non linear stochastic systems

H_\infty

Design and parameter identification of a general Hammerstein model

H∞ concept and a delay-derivative-dependent bounded real lemma (BRL), a robust actuator fault reconstruction is obtained wherein the effect of the uncertainty is minimized. Also, both the SMO and the BRL are delay-derivative-dependent which reduces the time-varying delay conservatism on the state estimation and on the fault reconstruction. A diesel engine system is included to illustrate the validity and the applicability of the proposed approaches.

Multiplicative Fault Estimation-Based Adaptive Sliding Mode Fault-Tolerant Control Design for Nonlinear Systems

This paper presents a recursive least-squares approach to estimate simultaneously the state and the unknown input of linear time varying discrete time systems with unknown input. The method is based on the assumption that no prior knowledge about the dynamical evolution of the input is available. The joint input and state estimation are obtained by recursive least-squares formulation by applying the inversion lemmas. The proposed filter is equivalent to recursive three step filter. To illustrate the performance of the proposed filter an example is given.

Robust State and Fault Estimation for Nonlinear Stochastic Systems with Unknown Disturbances

State estimation is of paramount importance in many fields of the problems encountered in practice. Filtering is the method of estimating the sate of the system by incorporating noisy observations. Particle filters are sequential Monte Carlo methods that use a point mass representation probability densities in order to propagate the required statistical proprieties for state estimation. In this paper, a new formulation of particle filter for nonlinear Bayesian estimation frameworks using various proposal importance function densities and state characterizations. New formulation particle filtering methods that use the extended and unscented Kalman filters are introduced. All the methods are compared in terms of accuracy and robustness. Is proposed from the sequential Bayesian approach theory. A synthetic stochastic model that incorporate non-linear, non stationarily is used for illustrative example.

Delay-dependant derivative sliding mode observer for output time-varying delay system: Network system application

A new approach for the design of robust H∞ sliding mode observer for Steer-by-Wire (SBW) systems with parametric uncertainties and sensor faults is proposed based on linear matrix inequalities (LMIs). The resulting H∞ observer guarantees asymptotic stability of the estimation error dynamics and is robust against parametric uncertainties. A sensor fault estimation scheme is presented where the estimated signal can approximate the fault signal to any accuracy. The effectiveness of the proposed approach is verified by simulations.

Recursive Five-Step Filter for State and Fault Estimation of Linear Descriptor Stochastic Systems with Unknown Disturbances

This paper deals with parameter identification of Hammer-stein model having a unified model of several discontinuous nonlinearities containing hysteresis, saturation, preload and dead-zone. This model contains different parameters the choice of which may generate nine different nonlinear-ities. Contrary to the nonlinearity block structure which is unknown, the structure of linear block is assumed to be known. The estimation of the nonlinearity selection parameters as well as the linear model parameters is ensured by recursive least squares method. This latter is tuned, so that it enables the estimation of internal variables relative to the selected nonlinearity. An illustrative example is presented to raise the efficiency of the proposed nonlinearity unified model.