Application of Naive Bayes Classifier Theorem in Detecting Induction Motor Bearing Failure

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Abstract
The usage of the induction motor is getting attention in the critical industrial process and leads to the necessity of identifying the current state of the motor. Usually, early detection of an incipient fault is done using condition monitoring. Bearing failure is the most common fault among induction motor and vibration analysis is often used for fault detection. The research work is carried out by giving scrutiny to the recognition of the current state of the motor. The two types of holes are induced on the outer raceway of the bearing and fast Fourier transform (FFT) analysis is performed to the load current of the stator. Additionally, diagnosis is performed using Naive Bayes Classifier Theorem (NBC) without considering the rotating speed. Furthermore, the comparison is done with the Support Vector Machine (SVM) and the results are promising.

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