Model-based Fault Detection and Isolation Method using ART2 Neural Network

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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