Transfer learning for bearing fault diagnosis: adaptive batch normalization and combined optimization method

Abstract

Bearings are crucial components in rotating machinery equipment. Bearing fault diagnosis plays a significant role in the maintenance of mechanical equipment. In practical industrial settings, equipment conditions often vary continuously, making it challenging to collect data for all operating conditions for bearing fault diagnosis. This paper proposes a transfer learning approach for bearing fault diagnosis based on Adaptive Batch Normalization (AdaBN) and a combined optimization algorithm. Initially, a ResNet neural network is trained using source domain data. Subsequently, the trained model is transferred to the target domain, where AdaBN is applied to mitigate domain shift issues. Furthermore, a combined optimization algorithm is employed during model training to enhance fault diagnosis accuracy. Experimental validation is conducted using bearing data from the CWRU dataset and NEFU dataset. Comparison shows that AdaBN and the combined optimization algorithm improve bearing fault diagnosis accuracy effectively. On the NEFU dataset, the diagnostic accuracy exceeds 95%.