Abstract
Kumar, Prashant Hati, Ananda ShankarCondition monitoring based on machine learning techniques for preventive maintenance of squirrel cage induction motors (SCIM) is the need of modern industries. Early detection of broken rotor bar (BRB) fault can reduce the unwanted production loss and minimizes downtime. With the advancement in high computational machines, machine learning techniques like logistic regression, artificial neural network, random forest technique, etc. can be efficiently implemented in BRB detection in SCIM. This paper deals with broken rotor bar detection in SCIM under different loading condition based on support vector machine (SVM)-based technique with the help of current spectrum analysis. Different kernel functions like linear, quadratic, cubic and Gaussian functions are analysed for finding the best kernel functions for achieving the good accuracy of the system.
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