✦ LIBER ✦

Model-based fault detection and isolation method using ART2 neural network

✍ Scribed by I. S. Lee; J. T. Kim; J. W. Lee; D. Y. Lee; K. Y. Kim

Publisher: John Wiley and Sons
Year: 2003
Tongue: English
Weight: 216 KB
Volume: 18
Category: Article
ISSN: 0884-8173
DOI: 10.1002/int.10134

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✦ Synopsis

This article presents a model-based fault diagnosis method to detect and isolate faults in the robot arm control system. The proposed algorithm is composed functionally of three main parts: parameter estimation, fault detection, and isolation. When a change in the system occurs, the errors between the system output and the estimated output cross a predetermined threshold, and once a fault in the system is detected, the estimated parameters are transferred to the fault classifier by the adaptive resonance theory 2 neural network (ART2 NN) with uneven vigilance parameters for fault isolation. The simulation results show the effectiveness of the proposed ART2 NN-based fault diagnosis method.

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