Sensor and Actuator Fault Diagnosis Based on Soft Computing Techniques

被引：3

作者：

Khireddine, Mohamed ^{[1
]}

Chafaa, Kheireddine ^{[1
]}

Slimane, Noureddine ^{[1
]}

Boutarfa, Abdelhalim ^{[1
]}

机构：

[1] Batna Univ, Elect Dept, LRP & LEA Labs, Chahid Boukhlouf St, Batna, Algeria

来源：

JOURNAL OF INTELLIGENT SYSTEMS | 2015年 / 24卷 / 01期

关键词：

Artificial neural network; fault detection and isolation; fuzzy logic; sliding mode observer; robotic manipulators;

D O I：

10.1515/jisys-2014-0037

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Computational intelligence techniques are being investigated as an extension of the traditional fault diagnosis methods. This article presents, for the first time, a scheme for fault detection and isolation (FDI) via artificial neural networks and fuzzy logic. It deals with the sensor fault of a three-link selective compliance assembly robot arm (SCARA) robot. A second scheme is proposed for fault detection and accommodation via analytical redundancy, and it deals with the sensor fault of a three-link SCARA robot. These proposed FDI approaches are implemented on Matlab/Simulink software and tested under several types of faults. The results show the importance of this process. Then, the sensor faults are detected and isolated successfully. Also, the actuator faults are detected and a fault tolerance strategy is used for reconfigurable control using a sliding-mode observer.

引用

页码：1 / 21

页数：21

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