Classifier Comparison for Failure Detection of Induction Motors Using Current Signal

Induction motor is widely used in the industry area and the bearing is one of the key mechanical components. The bearing minimizes the friction between the rotating part and stationary part of the rotating machine. It is important to monitor the bearing condition to give a warning before serious failures occur. The fault detection through electrical monitoring has been studied for the last several decades. Although they detect warning signs before serious problems occur, it does not always work when the sampling time is short. This research proposes a learning model for induction motor to diagnose bearing failures which learns features from electrical signatures. This experimental study uses data obtained from 415V, 55KW induction motor and clearance modified plain bearings.