Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

Abstract

Data Availability Statement: Data are contained within the article.Bearings are one of the critical components of rotating machinery, and their failure can cause catastrophic consequences. In this regard, previous studies have proposed a variety of intelligent diagnosis methods. Most existing bearing fault diagnosis methods implicitly assume that the training and test sets are from the same distribution. However, in real scenarios, bearings have been working in complex and changeable working environments for a long time. The data during their working processes and the data used for model training cannot meet this condition. This paper proposes an improved adversarial transfer network for fault diagnosis under variable working conditions. Specifically, this paper combines an adversarial transfer network with a short-time Fourier transform to obtain satisfactory results with the lighter network. Then, this paper employs a channel attention module to enhance feature fusion. Moreover, this paper designs a novel domain discrepancy hybrid metric loss to improve model transfer learning performance. Finally, this paper verifies the method’s effectiveness on three datasets, including dual-rotor, a Case Western Reserve University dataset and the Ottawa dataset. The proposed method achieves average accuracy, surpassing other methods, and shows better domain alignment capabilities.This work was supported in part by the Natural Science Foundation of China (No. 52175116), Major Research Programs of the Natural Science Foundation of China (No. 92060302), the Research Foundation of the Higher Educational Key Laboratory for Flexible Manufacturing Equipment Integration of Fujian Province, the Xiamen Institute of Technology, the National Key Science and Technology Infrastructure Opening Project Fund for Research and Evaluation facilities for Service Safety of Major Engineering Materials and the Aeronautical Science Foundation (No. 2019ZB070001). Also, this work was supported in part by the Royal Society award (number IEC\NSFC\223294) to Asoke K. Nandi. Jun Wang acknowledges the financial support from the Innovative Leading Talents Scholarship and Brunel University London