Name
Abstract | ||
|---|---|---|
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. (C) 2003 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1002/int.10134 | INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS |
Keywords | Field | DocType |
neural network,fault detection and isolation,control system,fault isolation,parameter estimation,robot arm | Data mining,Fault coverage,Control theory,Artificial intelligence,Estimation theory,Artificial neural network,System identification,Stuck-at fault,Adaptive resonance theory,Pattern recognition,Fault detection and isolation,Mathematics,Fault indicator | Journal |
Volume | Issue | ISSN |
18 | 10 | 0884-8173 |
Citations | PageRank | References |
7 | 0.97 | 3 |
Authors | ||
5 | ||