A novel sample selection approach based universal unsupervised domain adaptation for fault diagnosis of rotating machinery

Biliang Lu, Yingjie Zhang, Zhaohua Liu, Hualiang Wei and Qingshuai Sun

Reliability Engineering and System Safety, 2023, vol. 240, issue C

Abstract: Transfer learning-based fault diagnosis methods, especially unsupervised domain adaptation (UDA), have demonstrated significant potential in addressing insufficiently labeled signal problems. However, the assumption that the label spaces of two domains are identical may only be valid in some real-world scenarios. A priori information about the target domain's failure modes is usually unavailable in natural industries, limiting UDA's applicability. In this paper, a more common UDA scenario, called universal UDA (UUDA), is designed to handle domain and label space shift issues better, where no explicit assumption is made on the target label set. Furthermore, we propose a novel sample selection method to address the UUDA problem. Firstly, the outlier threshold learning aims to minimize the distance between known classes in the source domain while preserving the discrepancy between known and outlier classes. Subsequently, the domain-invariant sampler performs domain-invariant feature sampling while accommodating label space shifts. Lastly, an adversarial classifier training method is incorporated to enhance transferability by recognizing label space variability across domains. Extensive experiments have demonstrated exceptional performance in addressing domain and label space inconsistencies.

Keywords: Machinery fault diagnosis; Transfer learning; Deep learning; Unsupervised domain adaptation; Universal unsupervised domain adaptation (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S095183202300532X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:240:y:2023:i:c:s095183202300532x

DOI: 10.1016/j.ress.2023.109618

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().