Exploring the essence of compound fault diagnosis: A novel multi-label domain adaptation method and its application to bearings

Liuxing Chu; Qi Li; Bingru Yang; Liang Chen; Changqing Shen; Dong Wang

doi:10.1016/j.heliyon.2023.e14545

Exploring the essence of compound fault diagnosis: A novel multi-label domain adaptation method and its application to bearings

Heliyon. 2023 Mar 11;9(3):e14545. doi: 10.1016/j.heliyon.2023.e14545. eCollection 2023 Mar.

Authors

Liuxing Chu¹, Qi Li¹, Bingru Yang¹, Liang Chen¹, Changqing Shen¹, Dong Wang²

Affiliations

¹ School of Mechanical and Electric Engineering, Soochow University, Suzhou, 215000, China.
² The State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China.

Abstract

Compound fault diagnosis in essence is a fundamental but difficult problem to be solved. The separation and extraction of compound fault features remain great challenges in industrial applications due to the lack of labeled fault data. This paper proposes a novel multi-label domain adaptation method applicable to compound fault diagnosis of bearings. Firstly, multi-layer domain adaptation is designed based on a fault feature extractor with customized residual blocks. In that way, features from discrepant domain can be transformed into domain-invariant features. Furthermore, a multi-label classifier is applied to decompose compound fault features into corresponding single fault feature, and diagnoses them separately. The application on bearing datasets demonstrates that the proposed method could enhance the detachable degree of compound faults and achieve greater diagnostic performance than other existing methods.

Keywords: Compound fault diagnosis; Domain adaptation; Multi-label learning; Rolling bearing.