Abstract
Bearings are one of the most crucial components of any induction motor. The faults or failure of bearings can lead to high maintenance and operational costs. To ensure the induction motor operates consistently, it is crucial to identify and diagnose bearing faults at an early stage. In this perspective, this manuscript presents a method based on least square support vector machine (LSSVM) to identify the bearing defects of an induction motor. The IM parameters, such as stator currents, input voltage, and rotor speed, were extracted using experimental setup at different loads and health conditions of bearing. Then these parameters were used to train and validate the LSSVM algorithm in MATLAB to diagnose the bearing defects. The proposed method provides 97.14% fault prediction accuracy with an RBF kernel.
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