Bearing Fault Diagnosis of Switched Reluctance Motor in Electric Vehicle Powertrain via Multisensor Data Fusion

Abstract

A multisensor data fusion method is investigated for bearing fault diagnosis of a switched reluctance motor (SRM) of an electric vehicle (EV) powertrain under varying speed conditions. The accumulative rotation angle of the SRM rotor is estimated by fusing the synchronous sampled current and vibration signals. The time-domain vibration signal is then resampled on an angular domain, and the bearing fault type is identified on the envelope spectrum of the resampled signal. In this article, an experimental setup is designed to validate the performance of the proposed method compared with the traditional ones. The practical EV working conditions including driving, coasting, and braking are considered in the experiments. Results indicated that the proposed method successfully diagnoses the SRM bearing faults under random and complex conditions. The method is promising for online SRM fault diagnosis under varying speed conditions as it requires no extra tachometer, specifically when the sensorless control strategy is adopted.