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Induction Machine Fault Detection Using Generalized Feed Forward Neural Network
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 Title & Authors
Induction Machine Fault Detection Using Generalized Feed Forward Neural Network
Ghate, V.N.; Dudul, S.V.;
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 Abstract
Industrial motors are subject to incipient faults which, if undetected, 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 incipient fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based incipient 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. GFFD NN model is designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.
 Keywords
Induction motor;Fault detection;GFFDNN;PCA;
 Language
English
 Cited by
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A Fault Severity Index for Stator Winding Faults Detection in Vector Controlled PM Synchronous Motor, Journal of Electrical Engineering and Technology, 2015, 10, 6, 2326  crossref(new windwow)
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