Rolling bearing composite fault diagnosis based on stator currents

In the fault diagnosis of rolling bearings, compared to using vibration signals as the diagnostic target, utilising motor stator current signals can effectively reduce diagnostic costs and improve diagnostic efficiency. However, current signals often contain abundant noise that tends to obscure the characteristic signals related to bearing faults. Furthermore, the manifestation in signals of fault features for complex faults in rolling bearings is more intricate than that for single-point faults. Therefore, this paper proposes a signal processing method that can effectively suppress noise in current signals and further enhance the weak bearing composite fault features. The main content of this study leverages the inherent characteristics of motor stator current signals to eliminate a substantial amount of noise interference unrelated to fault characteristic signals, enabling the expression of weak fault characteristic signals to be further emphasised, thus achieving an effective representation of bearing composite fault features in current signals. Finally, the processed fault signals are used for fault classification through convolutional neural networks (CNNs). Experimental results effectively demonstrate that this method also has certain advantages and reliability in detecting and extracting weak compound fault features of bearings.