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

Author(s): Kim, J.T. | Kim, K.Y. | Lee, D.Y. | Lee, I.S. | Lee, J.W. |

Year: 2003

Citation: INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS Volume: 18 Issue: 10 Pages: 1087-1100

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.

Topics: Robotics, Applications: Industrial Control, Models: ART 2 / Fuzzy ART,

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