Induction Machine Fault Detection Using Support Vector Machine Based Classifier
نویسندگان
چکیده
Industrial motors are subject to various faults which, if unnoticed, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. Generalized Feed Forward (GFFDNN) and Support Vector Machine (SVM) NN models are designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.
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