Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems

被引：133

作者：

Li, P ^{[1
]}

Kadirkamanathan, V ^{[1
]}

机构：

[1] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S10 2TN, S Yorkshire, England

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS | 2001年 / 31卷 / 03期

关键词：

extended Kalman filter (EKF); fault diagnosis; likelihood ratio (LR) test; Monte-Carlo technique; nonlinear stochastic system; particle filter (PF);

D O I：

10.1109/5326.971661

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents the development of a particle filtering (PF) based method for fault detection and isolation (FDI) in stochastic nonlinear dynamic systems. The FDI problem is formulated in the multiple model (MM) environment, then by combining the likelihood ratio (LR) test with the PF, a new FDI scheme is developed. The simulation results on a highly nonlinear system are provided which demonstrate the effectiveness of the proposed method.

引用

页码：337 / 343

页数：7

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