A Migration Learning Method Based on Adaptive Batch Normalization Improved Rotating Machinery Fault Diagnosis

Abstract

Sustainable development has become increasingly important as one of the key research directions for the future. In the field of rotating machinery, stable operation and sustainable performance are critical, focusing on the fault diagnosis of component bearings. However, traditional normalization methods are ineffective in target domain data due to the difference in data distribution between the source and target domains. To overcome this issue, this paper proposes a bearing fault diagnosis method based on the adaptive batch normalization algorithm, which aims to enhance the generalization ability of the model in different data distributions and environments. The adaptive batch normalization algorithm improves the adaptability and generalization ability to better respond to changes in data distribution and the real-time requirements of practical applications. This algorithm replaces the statistical values in a BN with domain adaptive mean and variance statistics to minimize feature differences between two different domains. Experimental results show that the proposed method outperforms other methods in terms of performance and generalization ability, effectively solving the problems of data distribution changes and real-time requirements in bearing fault diagnosis. The research results indicate that the adaptive batch normalization algorithm is a feasible method to improve the accuracy and reliability of bearing fault diagnosis.