Predictive and Standalone Fault Diagnosis System for Induction Motors

Abstract

Sudden faults created in induction motors result in catastrophic failures and loss of production. Therefore, the industry is in need of a predictive based system that can identify developing faults in advance. Condition monitoring is used as the general method of identifying faults and taking measures before the dreadful situation. However, there is limited work done on the predictive methodologies based on the trend analysis. The study presented in this paper proposes a novel method that identifies trend variation of critical harmonics of the vibration spectrum with increasing fault severity for frequent mechanical faults; structural looseness, misalignment, bearing eccentricity and bearing inner race fault. Faults were artificially induced on a three-phase induction motor and vibration data obtained was analysed with a MATLAB based algorithm.