I.S. Lee, J.T. Kim, J.W. Lee, D.Y. Lee, Y.J. Lee, and K.Y. Kim (Korea)
Fault detection and isolation, parameter estimation, ART2 neural network, robot arm
A model-based fault diagnosis method to detect and isolate faults in the robot arm control system is proposed. The proposed algorithm is functionally composed 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 ART2 NN (adaptive resonance theory 2 neural network) with uneven vigilance parameters for fault isolation. The simulation results demonstrate the effectiveness of the proposed ART2 NN-based fault diagnosis method.
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