Bearing Fault Diagnosis under Time-Varying Speed and Load Conditions via Observer-Based Load Torque Analysis

Bearing fault is the most common failure in rotating machines, and bearing fault diagnosis (BFD) has been investigated using vibration, current, or acoustic signals. However, there are still challenges in some existing approaches. This study proposes a novel BFD method based on natural observer. Based on the analysis of the effects on the load torque signal caused by bearing faults in the permanent magnetic synchronous machine (PMSM), a modified natural observer was designed to reconstruct the load torque signal from electrical signals, acquiring a novel indicator without the additional sensor installed. Angular resampling was implemented to convert the non-stationary load torque signal into a stationary one to reduce the computational complexity. For full-auto diagnosis without human involvement, a threshold determination algorithm was also modified. Experimental validations were carried out under speed-varying and torque-varying conditions and were compared with phase current and q-axis current signals. The average signal-to-noise ratio (SNR) of the estimated load torque is about 8.65 times compared with the SNR of the traditional q-axis current. The effectiveness of the proposed method prior to the traditional PMSM bearing fault indicators is demonstrated by the order spectrum results.