Hybrid Neural-Fuzzy Network Based Fault Detection and Isolation System for DC Motor of Robot Manipulator
In this paper, the detecting and isolating fault that occurs in (actuator and sensor) in robot manipulator, which is used as a mathematical model were proposed for fault detection, where the neural network was used to detect the fault. The neural network was trained on the data set obtained from the Input /output on the (DC motor). The output of the sensor or actuator was compared with the output of the model (neural network) after that the residual signal is used to detect the fault. The fuzzy logic circuit was used for fault isolation that is depending on the residual signal from any sensor or actuator that faults. There are three types of faults detected and isolated in this study abrupt fault, incipient fault and intermittent fault. The Matlab R2012a was used to the model steady state designed and simulated . The model has a high capacity for detecting faults.
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