Finite Memory Observer for Switching Systems: Application to Diagnosis

Hocine, Abdelfettah; Maquin, Didier; Ragot, José

doi:10.3182/20050703-6-cz-1902.01870

Cited by 12 publications

(12 citation statements)

References 9 publications

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“…To deal with this problem, an unknown input finite memory observer is proposed for the identification of the actuator fault. Based on the matrices (21), the observer structure is carried out by treating the unknown component ∆u as part of an augmented state vector Hocine et al [2005]. In this procedure, an assumption is made that the actuator fault is constant or slowly time varying on the sliding window of size F .…”

Section: Actuator Fault Detection and Estimationmentioning

confidence: 99%

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Fault diagnosis for switching system using Observer Kalman filter IDentification

Akhenak

Bako

Duviella

et al. 2008

IFAC Proceedings Volumes

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Section: Actuator Fault Detection and Estimationmentioning

confidence: 99%

“…As the noises w k and v k are zero mean processes, an estimatex ′ s,k−F of the state at time step k − F , can easily be obtained as the solution of the following Least Squares criterion Hocine et al [2005]:…”

Section: Actuator Fault Detection and Estimationmentioning

confidence: 99%

Fault diagnosis for switching system using Observer Kalman filter IDentification

Akhenak

Bako

Duviella

et al. 2008

IFAC Proceedings Volumes

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“…Such an approach, by the use of classical estimators (e.g., bank of Kalman Filters) for the disturbance isolation, was tested however in [21][22][23] with the use of "fault signatures" table. Other works [24][25][26] use disturbance distribution matrices and apply observers for diagnosis [27,28].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Identification of the Input-Output Disturbances in Linear Dynamic Systems by the Use of the Exact Observation of the State

Byrski

2018

Mathematical Problems in Engineering

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A new methodology for the identification of the values of unknown disturbance signals acting in the input and output measurements of the dynamic linear system is presented. For the solution of this problem, the new idea of the use of two different state observers, which are coworking simultaneously in parallel, was elaborated. Special integral type observers are operating on the same finite time window of the width T and both can reconstruct the exact value of the vector state x(T) on the basis of input-output measurements in this interval [0, T]. If in the input-output signals the disturbances are absent (measurements of I/O signals are perfect) then both observers (although they are different) reconstruct the exact and the same state x(T). However, if in the measurement signals the disturbances are present then these observers will reconstruct the different values of the final states x1(T)=x(T)+e1(T) and x2(T)=x(T)+e2(T). It is because they have different norms and hence they generate different errors in both estimated states. Because of the disturbances, the real state x(T) is unknown, but it is easy to calculate the state difference: x1(T)-x2(T)=e1(T)-e2(T). It occurs that, based on this difference, the values of all the disturbances acting during the control process can be identified. In the paper, the theory of the exact state observation and application of such observers in online mode is recalled. The new methodology for disturbances identification is presented.

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“…Finite memory estimation is often accomplished according to the minimization of a least-squares criterion (see among others, [13] and [14]). Unfortunately, finite memory observers are often too sensitive to the output measurement noise [10]. In order to take advantages of both type of observers, this paper proposes a new observer structure combining the Luenberger observer with infinite memory and a Finite Memory observer throughout a simple weighting scheme and introduce this weighted observer in the framework of switching system estimation and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A discrete-time Sliding Window Observer for Markovian Switching System

Hocine

Chadli²,

Maquin

et al. 2006

Proceedings of the 45th IEEE Conference on Decision and Control

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In this paper, a fault detection method is developed for switching dynamic systems. These systems are represented by several linear models, each of them being associated to a particular operating mode. To finding the system operating mode the proposed method is based on mode probabilities and on a new structure of discrete-time observer with a sliding window measurements. This observer results from a combination of a Finite Memory Observer (FMO) and a Luenberger Observer. The stability condition of the observer is formulated in terms of linear matrix inequalities (LMI) using a quadratic Lyapunov function. The method also uses a priori knowledge information about the mode transition probabilities represented by a Markov chain. The proposed algorithm is of supervised nature where the faults to be detected are a priori indexed and modelled. In this work, the method is applied for the fault detection of a linear system characterized by a model of normal operating mode and several fault models. A comparison with the Generalized PseudoBayesian method shows the validity and some advantages of the suggested method.

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Finite Memory Observer for Switching Systems: Application to Diagnosis

Cited by 12 publications

References 9 publications

Fault diagnosis for switching system using Observer Kalman filter IDentification

Fault diagnosis for switching system using Observer Kalman filter IDentification

An Optimal Identification of the Input-Output Disturbances in Linear Dynamic Systems by the Use of the Exact Observation of the State

A discrete-time Sliding Window Observer for Markovian Switching System

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