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:
-
- Temasek Laboratories, National University of Singapore, Singapore 117508 (Singapore)
- 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}
}