Detection of localized bearing faults in PMSMs by means of envelope analysis and wavelet packet transform using motor speed and current signals

The reliability of machinery plays an essential role in industrial practice, which includes the growing topic of fault detection for electric motors. Since permanent magnet synchronous motor (PMSMs) usually have built-in current and speed sensors, it is advantageous to use them for fault detection purposes as they enable a non-invasive and cost-effective implementation. Focusing on speed and current signals, two methods are developed for the detection of localized bearing faults in this paper. They leverage envelope analysis and the wavelet packet transform for feature extraction before classification is performed using a support vector machine. In addition, properties of vibration signals and built-in sensor signals are discussed and important similarities are highlighted. The effectiveness of the two methods is demonstrated in the analysis of experimental measurements, where non-artificial localized bearing faults were investigated by means of the phase currents, the d-current and q-current as well as the speed signal along with a comparison to vibration signal analysis. Both methods are shown to be effective for bearing fault diagnosis and exhibit higher detection accuracies than comparable approaches, with the best results being achieved using the q-current. This highlights the viability of built-in sensors in this context.