Chu L, Li Q, Yang B, Chen L, Shen C, Wang D. Exploring the essence of
compound fault diagnosis: A novel multi-label domain adaptation method and its application to bearings.
Heliyon 2023;
9:e14545. [PMID:
36950628 PMCID:
PMC10025141 DOI:
10.1016/j.heliyon.2023.e14545]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 03/05/2023] [Accepted: 03/09/2023] [Indexed: 03/13/2023] Open
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.
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