Generalized likelihood ratio approach for fault detection in linear dynamic stochastic systems with unknown inputs

Keller, Jean-Yves; Summerer, Leonhard; Boutayeb, Mohamed; Darouach, Mohamed

doi:10.1080/00207729608929330

Cited by 15 publications

(8 citation statements)

References 7 publications

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“…Step 1: Obtain ( ) Step 2: Calculate the likelihood of part of the new measurement given the measurement up to instant k. This is the main difference between standard JMLS filter and the filter considering unknown input. Because of the existence of unknown input d(k), it becomes impossible to obtain the margin density of Ty(k+1), where T is the basis of the left null space of CF , can be obtained now (Keller et al 1996). In (Keller et al 1996), the likelihood of Ty(k+1) given the history measurement is: Step 3: Calculate the weights ( )…”

Section: Bayesian Filter For Ncs Based On Uikfmentioning

confidence: 99%

Fault detection and isolation for a kind of NCSs with Markov Delays

Cheng

Wang

et al. 2008

IFAC Proceedings Volumes

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Section: Bayesian Filter For Ncs Based On Uikfmentioning

confidence: 99%

Fault detection and isolation for a kind of NCSs with Markov Delays

Cheng

Wang

et al. 2008

IFAC Proceedings Volumes

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“…In [11,12], a threshold is set by using various kind of signal norms. A statistical threshold computation has been studied, for instance, [13][14][15]. It should be emphasize here that setting a threshold is an extremely important stage for an observer-based FD technique.…”

Section: Introductionmentioning

confidence: 99%

“…It can be seen that LMJS can be considered as a stochastic system, where an abrupt change detection within system can be obtained by a hypothesis testing. A so-called generalized likelihood ratio [33] is the most popular method to set a threshold for a purpose of FD and has been reported in [7,15] and references therein. However, this method requires a probability density function of residual signal to be known before and after an occurrence of faults [34].…”

Section: Introductionmentioning

confidence: 99%

Threshold computation for fault detection in linear discrete‐time Markov jump systems

Saijai

Ding

Abdo

et al. 2013

Adaptive Control & Signal

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This paper proposes a threshold computation scheme for an observer-based fault detection (FD) in linear discrete-time Markovian jump systems. An observer-based FD scheme typically consists of two stages known as residual generation and residual evaluation. Even information of faults is contained inside a residual signal, a decision of faults occurrence is consequently made by a residual evaluation stage, which consists of residual evaluation function and threshold setting. For this reason, a successful FD strongly depends on a threshold setting for a given residual evaluation function. In this paper, Kalman filter (KF) is used as a residual generator. Based on an accessibility of Markov chain to KF, two types of residual generations are considered, namely mode-dependent and mode-independent residual generation. After that threshold is computed in a residual evaluation stage such that a maximum fault detection rate is achieved, for a given false alarm rate. Without any knowledge of a probability density function of residual signal before and after fault occurrence, a threshold is computed by using an estimation of residual evaluation function variance in a fault-free case. Finally, a detection performance is demonstrated by a numerical example.

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“…The estimate is essentially a weighted average of the particles representing the underlying distribution [6,8] …”

Section: Augmented States Model For Fault Isolationmentioning

confidence: 99%

“…Different types of approaches appearing in literature, as can be seen from a large number of survey papers [1][2][3][4][5][6]. The problem of fault detection can be roughly divided into two major categories: First, we need to estimate the unknown and un measurable state variable of model and generate residuals on the basis of the available observations and a model of the system.…”

Section: Introductionmentioning

confidence: 99%

Particle Filter Approach to Fault Detection andIsolation in Nonlinear Systems

Souibgui¹,

Hmida²,

Châari³

2012

AJSP

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This paper introduces the particle-filtering (PF) based framework for fault diagnosis in non-linear systems and noise and disturbances being Gaussian. In this paper, we use the sequential Monte Carlo filtering approach where the complete posterior distributions of the estimates are represented through samples or particles as opposed to the mean and covariance of an approximated Gaussian distribution. We compare the fault detection performance with that using the extended Kalman filtering and investigate the isolation performance on a nonlinear system.

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Generalized likelihood ratio approach for fault detection in linear dynamic stochastic systems with unknown inputs

Cited by 15 publications

References 7 publications

Fault detection and isolation for a kind of NCSs with Markov Delays

Fault detection and isolation for a kind of NCSs with Markov Delays

Threshold computation for fault detection in linear discrete‐time Markov jump systems

Particle Filter Approach to Fault Detection andIsolation in Nonlinear Systems

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