Finite memory generalised state observer for failure detection in dynamic systems

Nuninger, Walter; Kratz, Frédéric; Ragot, José

doi:10.1109/cdc.1998.760741

Cited by 16 publications

(10 citation statements)

References 4 publications

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“…One can find only few publications in which such an approach is presented (e.g., Medvedev, 1996;Nuninger et al, 1998). Commonly for diagnostic purposes, the asymptotic Luenberger estimator is applied.…”

Section: Discussionmentioning

confidence: 99%

A double window state observer for detection and isolation of abrupt changes in parameters

Byrski

2016

International Journal of Applied Mathematics and Computer Science

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The paper presents a new method for diagnosis of a process fault which takes the form of an abrupt change in some real parameter of a time-continuous linear system. The abrupt fault in the process real parameter is reflected in step changes in many parameters of the input/output model as well as in step changes in canonical state variables of the system. Detection of these state changes will enable localization of the faulty parameter in the system. For detecting state changes, a special type of exact state observer will be used. The canonical state will be represented by the derivatives of the measured output signal. Hence the exact state observer will play the role of virtual sensors for reconstruction of the derivatives of the output signal. For designing the exact state observer, the model parameters before and after the moment of fault occurrence must be known. To this end, a special identification method with modulating functions will be used. A novel concept presented in this paper concerns the structure of the observer. It will take the form of a double moving window observer which consists of two signal processing windows, each of width T . These windows are coupled to each other with a common edge. The right-hand side edge of the left-side moving window in the interval [t − 2T, t − T ] is connected to the left-hand side edge of the right-side window which operates in the interval [t − T, t]. The double observer uses different measurements of input/output signals in both the windows, and for each current time t simultaneously reconstructs two values of the statethe final value of the state in the left-side window zT (t − T ) and the initial value of the state z0(t − T ) in the right-side window. If the process parameters are constant, the values of both the states on the common joint edge are the same. If an abrupt change (fault) in some parameter at the moment tA = t − T occurs in the system, then step changes in some variables of the canonical state vector will also occur and the difference between the states will be detected. This will enable localization of the faulty parameter in the system.

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Section: Discussionmentioning

confidence: 99%

A double window state observer for detection and isolation of abrupt changes in parameters

Byrski

2016

International Journal of Applied Mathematics and Computer Science

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“…Clearly, the results of the suggested method are better than those of GPB1 method. This fact can 45th IEEE CDC, San Diego, USA, Dec. [13][14][15]2006 ThA09.4 be explained by the sensitivity of GPB1 to the noise affecting the system state and measurement (the signal to noise ratio in the example is equal to 10%). This allows us to conclude that the use of SWO, for fault detection in switching systems, is less sensitive to noise than traditional GPB methods.…”

Section: An Application Examplementioning

confidence: 96%

“…To overcome this drawback, another class of observers using finite number of delayed input/output measurements on a finite time window was also defined. These observers are denoted using various terms such as finite-memory, receding-horizon or sliding-window estimation [15], [16] and [11]. Finite memory estimation is often accomplished according to the minimization of a least-squares criterion (see among others, [13] and [14]).…”

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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“…Toivonen in [12,13] continued the study of such mixed types of state observers and called them "finite memory deadbeat observers." Some other version of the finite memory deadbeat observer was also presented in [14]. However, all of the above observer's versions were based on standard least squares approach and are only subcases of the general form of the exact and optimal state observer.…”

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 generalised state observer for failure detection in dynamic systems

Cited by 16 publications

References 4 publications

A double window state observer for detection and isolation of abrupt changes in parameters

A double window state observer for detection and isolation of abrupt changes in parameters

A discrete-time Sliding Window Observer for Markovian Switching System

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

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