A robust fault detection and isolation method via sliding mode observer

Junzheng Wang; Jiangbo Zhao; Liling Ma

A robust fault detection and isolation method via sliding mode observer

Junzheng Wang^*, Jiangbo Zhao, Liling Ma

^*Corresponding author for this work

School of Automation

Research output: Contribution to conference › Paper › peer-review

3 Citations (Scopus)

Abstract

A robust fault detection and isolation (FDI) approach for a class of nonlinear systems with uncertainty was presented. The FDI scheme was based on sliding mode observer, which was robust against system uncertainty. Fault detection can be realized by use of sliding boundary size. When the fault had been detected, the estimate part in the observer for the fault can be enabled. A radial basis function (RBF) neural network was used to approximate the fault, so making the fault isolation a simple task. The theoretic analysis guaranteed the convergence of the observer. Simulation results show the feasibility of the proposed approach.

Original language	English
Pages	1727-1730
Number of pages	4
Publication status	Published - 2004
Event	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings - Hangzhou, China Duration: 15 Jun 2004 → 19 Jun 2004

Conference

Conference	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings
Country/Territory	China
City	Hangzhou
Period	15/06/04 → 19/06/04

Keywords

Fault detection
Fault isolation
Nonlinear system
RBF neural network
Robustness

Cite this

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title = "A robust fault detection and isolation method via sliding mode observer",

abstract = "A robust fault detection and isolation (FDI) approach for a class of nonlinear systems with uncertainty was presented. The FDI scheme was based on sliding mode observer, which was robust against system uncertainty. Fault detection can be realized by use of sliding boundary size. When the fault had been detected, the estimate part in the observer for the fault can be enabled. A radial basis function (RBF) neural network was used to approximate the fault, so making the fault isolation a simple task. The theoretic analysis guaranteed the convergence of the observer. Simulation results show the feasibility of the proposed approach.",

keywords = "Fault detection, Fault isolation, Nonlinear system, RBF neural network, Robustness",

author = "Junzheng Wang and Jiangbo Zhao and Liling Ma",

year = "2004",

language = "English",

pages = "1727--1730",

note = "WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings ; Conference date: 15-06-2004 Through 19-06-2004",

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TY - CONF

T1 - A robust fault detection and isolation method via sliding mode observer

AU - Wang, Junzheng

AU - Zhao, Jiangbo

AU - Ma, Liling

PY - 2004

Y1 - 2004

N2 - A robust fault detection and isolation (FDI) approach for a class of nonlinear systems with uncertainty was presented. The FDI scheme was based on sliding mode observer, which was robust against system uncertainty. Fault detection can be realized by use of sliding boundary size. When the fault had been detected, the estimate part in the observer for the fault can be enabled. A radial basis function (RBF) neural network was used to approximate the fault, so making the fault isolation a simple task. The theoretic analysis guaranteed the convergence of the observer. Simulation results show the feasibility of the proposed approach.

AB - A robust fault detection and isolation (FDI) approach for a class of nonlinear systems with uncertainty was presented. The FDI scheme was based on sliding mode observer, which was robust against system uncertainty. Fault detection can be realized by use of sliding boundary size. When the fault had been detected, the estimate part in the observer for the fault can be enabled. A radial basis function (RBF) neural network was used to approximate the fault, so making the fault isolation a simple task. The theoretic analysis guaranteed the convergence of the observer. Simulation results show the feasibility of the proposed approach.

KW - Fault detection

KW - Fault isolation

KW - Nonlinear system

KW - RBF neural network

KW - Robustness

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M3 - Paper

AN - SCOPUS:4444329499

SP - 1727

EP - 1730

T2 - WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings

Y2 - 15 June 2004 through 19 June 2004

ER -

A robust fault detection and isolation method via sliding mode observer

Abstract

Conference

Keywords

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