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

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3 Author(s)
Yumin Zhang ; Temasek Laboratories, National University of Singapore, Singapore ; Qing-Guo Wang ; Kai-Yew Lum

In this paper, a 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 weighted average function is given as an integral form 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 adaptive fault diagnosis algorithm is further investigated to estimate the fault. The simulation example given demonstrates the effectiveness of the proposed approaches.

Published in:

2008 IEEE International Conference on Automation and Logistics

Date of Conference:

1-3 Sept. 2008