Delay-dependent fault detection and diagnosis using B-spline neural networks and nonlinear filters for time-delay stochastic systems

被引：8

作者：

Li, Tao ^{[1
]}

Yi, Yang ^{[1
]}

Guo, Lei ^{[2
]}

Wang, Hong ^{[3
]}

机构：

[1] Southeast Univ, Res Inst Automat, Nanjing, Peoples R China

[2] Beihang Univ, Sch Instrument Sci & Optoelect Engn, Beijing 100083, Peoples R China

[3] Univ Manchester, Dept Elect Engn & Elect, Manchester M60 1QD, Lancs, England

来源：

NEURAL COMPUTING & APPLICATIONS | 2008年 / 17卷 / 04期

关键词：

D O I：

10.1007/s00521-007-0141-7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new fault detection and diagnosis (FDD) scheme is studied in this paper for the continuous-time stochastic dynamic systems with time delays, where the available information for the FDD is the input and the measured output probability density functions (PDFs) of the system. The square-root B-spline neural networks is used to formulate the output PDFs with the dynamic weightings. As a result, the concerned FDD problem can be transformed into a robust FDD problem subjected to a continuous time uncertain nonlinear system with time delays. Delay-dependent criteria to detect and diagnose the system fault are provided by using linear matrix inequality (LMI) techniques. It is shown that this new criterion can provide higher sensitivity performance than the existing result. Simulations are given to demonstrate the efficiency of the proposed approach.

引用

页码：405 / 411

页数：7

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