A Hierarchical Approach for the Recognition of Induction Machine Failures

Abstract

Due to its sturdiness, low cost and ease of implementation, the induction machine is one of the most common electrical motors used in industry. However, this machine still concedes failures leading to unplanned shutdowns, sources of significant financial losses. For this reason, induction machine failure diagnosis has become an ordinary task, where some ameliorations have to be achieved in order to improve the efficiency of maintenance programs by minimizing the number and the time period of unexpected shutdowns. In this paper, we address the recognition of inter-turn short circuit (ITSC) in the stator windings and broken rotor bars (BRBs) in the three-phase squirrel cage induction machine by proposing a new approach. This approach relies on the three-phase current analysis method to extract features and a new hierarchical recognition algorithm based on an ensemble of three different classifiers. In addition, the present work studies the failures recognition of the induction machine operating with a torque and speed control. The proposed approach is a major advanced for the induction machine diagnosis research because it recognizes correctly (detects, localizes and estimates the degree of severity) ITSC and (detects and estimates) BRB faults with an accuracy of 93.36% and with a great robustness compared to a classical machine learning algorithm.