Modified DSAN for unsupervised cross-domain fault diagnosis of bearing under speed fluctuationShow others and affiliations
2022 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 65, p. 180-191Article in journal (Refereed) Published
Abstract [en]
Existing researches about unsupervised cross-domain bearing fault diagnosis mostly consider global alignment of feature distributions in various domains, and focus on relatively ideal diagnosis scenario under the steady speeds. Therefore, unsupervised feature adaptation between all the corresponding subdomains under speed fluctuation remains great challenges. This paper proposes a modified deep subdomain adaptation network (MDSAN) for more practical and challenging cross-domain diagnostic scenarios from the fluctuating speeds to steady speeds. Firstly, to extract the representative features and effectively suppress negative transfer, a novel shared feature extraction module guided by multi-headed self-attention mechanism is constructed. Then, a new trade-off factor is designed to improve the convergence performance and optimization process of MDSAN. The proposed method is used for analyzing experimental bearing vibration data, and the results show that it has higher diagnostic accuracy, faster convergence, better distribution alignment, and is more suitable for unsupervised cross-domain fault diagnosis under speed fluctuation scenario compared with the existing methods.
Place, publisher, year, edition, pages
Elsevier B.V. , 2022. Vol. 65, p. 180-191
Keywords [en]
Cross-domain bearing fault diagnosis, Modified deep subdomain adaptation network, Multi-headed self-attention mechanism, New trade-off factor, Speed fluctuation
National Category
Computer Sciences
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-93768DOI: 10.1016/j.jmsy.2022.09.004ISI: 000911575300007Scopus ID: 2-s2.0-85138051221OAI: oai:DiVA.org:ltu-93768DiVA, id: diva2:1707711
Note
Validerad;2022;Nivå 2;2022-11-01 (joosat);
Funder: Natural Science Fund for Excellent Young Scholars of Hunan Province (2021JJ20017); National Natural Science Foundation of China (51905160); Student Innovation Training Program (S202110532210)
2022-11-012022-11-012023-05-08Bibliographically approved