Particle filtering-based fault detection in non-linear stochastic systems

Kadirkamanathan, Visakan; Li, Ping; Jaward, Mohamed Hisham; Fabri, Simon G.

doi:10.1080/00207720110102566

Cited by 78 publications

(57 citation statements)

References 21 publications

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“…For the purpose of fault detection, the method proposed in this paper is to design several particle filters, each assuming a different subset of the possible faults formulated in system described by (1) and (2). The advantages of using the complete probability density function of the system state in fault detection is bound to be superior to one which uses approximations, such as in the extended kalman filter (EKF).…”

Section: Particle Filter For Fault Detectionmentioning

confidence: 99%

“…Problem of fault detection and isolation (FDI) in dynamic systems has received considerable attention for many years due to icreasingly complex systems and reliability [2]. Different types of approaches appearing in literature, as can be seen from a large number of survey papers [1][2][3][4][5][6].…”

Section: Introductionmentioning

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%

“…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. Secondly, we need to decide on the occurrence of a fault based on the residuals generated [2]. For the stochastic systems, much of development in fault detection schemes has relied on the system being linear and disturbances being Gaussian.…”

Section: Introductionmentioning

confidence: 99%

“…In year 2000 Kadirkamanathan and al. [2] introduced Sequential Monte Carlo methods into field of fault detection and isolation(FDI). Different types of approaches appearing in literature of FDI problem [1,3] for solving general nonlinear systems with known sets of possible faults.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Particle Filter For Fault Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Particle Filter Approach to Fault Detection andIsolation in Nonlinear Systems

Souibgui¹,

Hmida²,

Châari³

2012

AJSP

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Estimation of structural changes in nonlinear time series models by using particle filters and genetic programming

Tokinaga

2015

Adaptive Control & Signal

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Several works have demonstrated detection of changes of state equations (called structural changes) based on statistical measures but have given no suggestions regarding the functional forms of the state equations after changes. This paper deals with the estimation of structural changes in nonlinear time series models by using particle filters, genetic programming (GP), and its applications. We consider the problems of state estimation from the observed time series that are generated based on nonlinear state equations. It is assumed that structural changes can be detected by some measure of likelihood and that the state equation after changes is modified from its current functional form. Individuals corresponding to functional forms in the GP pool are generated at random, and we apply the crossover operation between the current functional form and the individuals by giving possible multiple functional forms. Then, we have the optimal functional form among the possible functional forms generated by GP from the current form. As an application, we show the estimation of structural change for an artificially generated time series and also discuss the estimation of functional forms for a real economic time series before and after structural changes.In previous studies, several different indicators have been applied for the detection of structural changes in time series generation models. However, in this paper, we aim to estimate the time direction changes from the time of structural change. We assume a nonlinear equation in the PFs, is deformed, and is derived, from the current function .C /, to estimate these multiple deformed functions .A 1 ; A 2 ; :::/ by crossover processing using the GP method [10,[18][19][20][21][22][23][24]. That is, a set of functions, randomly generated in individual pool of GP, can be considered as opponent functions of the transformation of function C . By using the crossover procedure, we can only determine the number of individuals in a population pool that is a plurality of the functional form that is deformed from function C . The maximum likelihood, corresponding to the best functional form A m of the model, for which the error is minimized as a function of the changed structure, can be found by applying the PFs for all this generated function A i . As an application, we use artificial data to verify the effectiveness of this method, and furthermore, we applied this method to real-world time series data and discuss the estimation of the state equation after the structural change.In the following, Section 2 describes the basics of function estimation and extraction of structural changes when using PFs. Section 3 presents the equation estimation by function deformation and GP methods, and examples of the application are given in Section 4. Related worksIn the following, we summarize the related works regarding change detection of nonlinear dynamics and stress the strengths of our paper. Research on the estimation of structural changes (or change point detection) for nonlinear dynamics a...

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A Novel Fuzzy Approach for State Estimation of Nonlinear Hybrid Systems Using Particle Filtering Method

Salahshoor

Samadi

2011

Asian Journal of Control

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Reliable state estimation is challenging for nonlinear hybrid systems. Particle filtering has emerged as an appealing approach for online hybrid state estimation. Mode detection in nonlinear hybrid systems is, however, a troublesome issue for the conventional particle filter mainly due to sample impoverishment. The problem is also exacerbated when dynamics that govern healthy or faulty modes are close together. False mode detection consequently leads to erroneous continuous state estimation. This paper proposes a novel fuzzy-based particle filter to reduce continuous state estimation errors due to failures in mode detection. It is fulfilled by considering a fuzzified contribution of each feasible mode in overall estimation. In addition, two new resampling strategies are presented to tackle the degeneracy problem. A set of simulation test studies are conducted to extract the characteristic features and evaluate the performance of the proposed algorithm compared to observation and transition-based most likely modes tracking particle filter (OTPF) as one of the most meticulous proposed estimation algorithms. The simulation results demonstrate the superior efficiency of the algorithm in dealing with the considered potential estimation problems.

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Particle filtering-based fault detection in non-linear stochastic systems

Cited by 78 publications

References 21 publications

Particle Filter Approach to Fault Detection andIsolation in Nonlinear Systems

Particle Filter Approach to Fault Detection andIsolation in Nonlinear Systems

Estimation of structural changes in nonlinear time series models by using particle filters and genetic programming

A Novel Fuzzy Approach for State Estimation of Nonlinear Hybrid Systems Using Particle Filtering Method

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