Original scientific paper

https://doi.org/10.7305/automatika.53-3.130

Fault Detection and Diagnosis for Non-Gaussian Singular Stochastic Distribution Systems via Output PDFs

Qu Yi ; College of Electrical and Information Engineering, Lanzhou University of Technology, Langongping Road 287, Qilihe District, 730050,Lanzhou, Gansu, P.R. China
Li Zhan-ming ; College of Electrical and Information Engineering, Lanzhou University of Technology, Langongping Road 287, Qilihe District, 730050,Lanzhou, Gansu, P.R. China
Li Er-chao ; College of Electrical and Information Engineering, Lanzhou University of Technology, Langongping Road 287, Qilihe District, 730050,Lanzhou, Gansu, P.R. China

Abstract

This paper investigates the problem of fault detection and diagnosis (FDD) problem for non-Gaussian singular stochastic distribution control (SDC) systems via the output probability density functions(PDFs). The PDFs can be approximated by using square-root B-spline expansion, via this expansions to represent the dynamics weighting systems between the system input and the weights related to the output PDFs. In this work, an optimal fault detection and diagnosis algorithm is presented by introducing the parameter-updating. When the fault occurs, an adaptive network parameter-updating law is designed to approximated the fault. Finally, the simulation result are given to show that the approach can detect fault and estimate the size of fault.

Keywords

Probability density fuctions; Non-gausian singular stochastic distribution control; Fault detection and diagnosis; Adaptive network parameter-updating

Hrčak ID:

89245

URI

https://hrcak.srce.hr/89245

Publication date:

20.8.2012.

Article data in other languages: croatian

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