Fault Detection and Isolation Problem: Sliding Mode Fuzzy Observers and Neural Networks

被引：0

作者：

Anzurez-Marin, Juan ^{[1
]}

Espinosa-Juarez, Elisa ^{[1
]}

Castillo-Toledo, Bernardino ^{[2
]}

机构：

[1] Fac Univ Michoacana San Nicolas de Hidalgo, Elect Engn, Morelia, Michoacan, Mexico

[2] CINVESTAV IPN Unidad Guadalajara, Dept Automat Control, Guadalajara, Jalisco, Mexico

来源：

2014 11TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, COMPUTING SCIENCE AND AUTOMATIC CONTROL (CCE) | 2014年

关键词：

Fault diagnosis; Takagi-Sugeno fuzzy models; Sliding mode observers; neural networks; REDUNDANCY; DIAGNOSIS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper results of the application of a hybrid Fault Detection and Isolation scheme are presented. A Takagi-Sugeno fuzzy model is used to describe the system and a type of sliding mode observers are designed to estimate the system state vector; from this, the diagnostic signal-residual is generated by the comparison of measured and estimated output. Neural Networks are proposed in order to solve the fault isolation problem based on signal-residual. The faulted component is identified from the active signal-residuals by means of the application of the presented technique based on neural networks. This paper shows an application of the fault diagnosis technique, which was satisfactorily tested in a two-tank hydraulic system.

引用

页数：7

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