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Title: A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation

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.

Authors:
;  [1];  [2]
  1. Temasek Laboratories, National University of Singapore, Singapore 117508 (Singapore)
  2. Depa. Electrical and Computer Engineering, National University of Singapore, Singapore 117576 (Singapore)
Publication Date:
OSTI Identifier:
21293339
Resource Type:
Journal Article
Journal Name:
AIP Conference Proceedings
Additional Journal Information:
Journal Volume: 1107; Journal Issue: 1; Conference: CISA'09: 2. Mediterranean conference on intelligent systems and automation, Zarzis (Tunisia), 23-25 Mar 2009; Other Information: DOI: 10.1063/1.3106516; (c) 2009 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-243X
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ALGORITHMS; APPROXIMATIONS; DENSITY FUNCTIONAL METHOD; DIAGNOSIS; FAULT TREE ANALYSIS; FILTERS; INTEGRALS; LEAST SQUARE FIT; NONLINEAR PROBLEMS; PROBABILITY; PROBABILITY DENSITY FUNCTIONS; SIGNALS; SIMULATION; SPLINE FUNCTIONS; STOCHASTIC PROCESSES

Citation Formats

Yumin, Zhang, Lum, Kai-Yew, and Qingguo, Wang. A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation. United States: N. p., 2009. Web. doi:10.1063/1.3106516.
Yumin, Zhang, Lum, Kai-Yew, & Qingguo, Wang. A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation. United States. https://doi.org/10.1063/1.3106516
Yumin, Zhang, Lum, Kai-Yew, and Qingguo, Wang. 2009. "A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation". United States. https://doi.org/10.1063/1.3106516.
@article{osti_21293339,
title = {A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation},
author = {Yumin, Zhang and Lum, Kai-Yew and Qingguo, Wang},
abstractNote = {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.},
doi = {10.1063/1.3106516},
url = {https://www.osti.gov/biblio/21293339}, journal = {AIP Conference Proceedings},
issn = {0094-243X},
number = 1,
volume = 1107,
place = {United States},
year = {Thu Mar 05 00:00:00 EST 2009},
month = {Thu Mar 05 00:00:00 EST 2009}
}