Clustering-guided novel unsupervised domain adversarial network for partial transfer fault diagnosis of rotating machinery

Cao, Hongru and Shao, Haidong and Liu, Bin and Cai, Baoping and Cheng, Junsheng (2022) Clustering-guided novel unsupervised domain adversarial network for partial transfer fault diagnosis of rotating machinery. IEEE Sensors Journal. ISSN 1530-437X (In Press)

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Abstract

Unsupervised partial transfer fault diagnosis studies of rotating machinery have practical significance, which still exists some challenges, for example, the learned domain-specific statistics and parameters usually influence the learning effect of target-domain features to some degree, and the relatively scattered target-domain features will lead to negative transfer. To overcome those limitations and further improve partial transfer fault diagnosis performance, a clustering-guided novel unsupervised domain adversarial network is proposed in this paper. Firstly, a novel unsupervised domain adversarial network is constructed using domain-specific batch normalization to remove domain-specific information to enhance alignment between source and target domains. Secondly, embedded clustering strategy is designed to learn tightly clustered target-domain features to suppress negative transfer in partial domain adaptation process. Finally, a joint optimization objective function is defined to balance different losses to improve the training and diagnosis performance. Two experimental cases of bevel gearbox and bearing are used to validate the effectiveness and superiority of the proposed method in solving unsupervised partial transfer fault diagnosis problems.

ORCID iDs

Cao, Hongru, Shao, Haidong, Liu, Bin ORCID logoORCID: https://orcid.org/0000-0002-3946-8124, Cai, Baoping and Cheng, Junsheng;
  • Item type:Article
    ID code:81073
    Dates:
    Date
    Event
    7 June 2022
    Published
    7 June 2022
    Accepted
    Notes:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Strathclyde Business School > Management Science
    Depositing user: Pure Administrator
    Date deposited:14 Jun 2022 08:36
    Last modified:11 Nov 2024 13:31
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/81073
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