Electric motor defects diagnosis based on kernel density estimation and Kullback-Leibler divergence in quality control scenario

The present paper deals with the defect detection and diagnosis of induction motor, based on motor current signature analysis in a quality control scenario. In order to develop a monitoring system and improve the reliability of induction motors, Clarke-Concordia transformation and kernel density estimation are employed to estimate the probability density function of data related to healthy and faulty motors. Kullback-Leibler divergence identifies the dissimilarity between two probability distributions and it is used as an index for the automatic defects identification. Kernel density estimation is improved by fast Gaussian transform. Since these techniques achieve a remarkable computational cost reduction respect the standard kernel density estimation, the developed monitoring procedure became applicable on line, as a Quality Control method for the end of production line test. Several simulations and experimentations are carried out in order to verify the proposed methodology effectiveness: broken rotor bars and connectors are simulated, while experimentations are carried out on real motors at the end of production line. Results show that the proposed data-driven diagnosis procedure is able to detect and diagnose different induction motor faults and defects, improving the reliability of induction machines in quality control scenario. © 2015 Elsevier Ltd.