A H-infinity fault detection and diagnosis scheme for discrete nonlinear system using output probability density estimation

Yumin Zhang, Qing Guo Wang, Kai Yew Lum

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper, a H-infinity fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus, the classical nonlinear filter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive fault diagnosis algorithm is further investigated to estimate the fault. Simulation example is given to demonstrate the effectiveness of the proposed approaches.

Original languageEnglish
Pages (from-to)79-84
Number of pages6
JournalAIP Conference Proceedings
Volume1107
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2nd Mediterranean Conference on Intelligent Systems and Automation, CISA 2009 - Zarzis, Tunisia
Duration: 23 Mar 200925 Mar 2009

Keywords

  • B-spline expansions
  • Fault detection and diagnosis
  • Filter design
  • Probability density function

ASJC Scopus subject areas

  • General Physics and Astronomy

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