Multisource Unsupervised Subdomain Adaptation Network for Rotary Machine Fault Diagnosis

Authors:

21 March 2023

journal article
research article
Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement

Vol. 72 (00189456) , 1-11
DOI:10.1109/tim.2023.3259019

Abstract

Domain adaptation-based methods have been widely developed for fault diagnosis. However, the existing approaches mainly focus on the global distribution alignment of single-to-single domain without considering multiple scenarios, and overlooking the alignment of subdomains, which causes misclassification near the class boundaries. Thus, a multisource unsupervised subdomain adaptation network is proposed in this paper to solve fault diagnosis of rotary machines under multiple and variable working conditions. Using data images as inputs, a domain feature extractor is constructed to extract the domain-invariant features and map each source-target pair into the advanced feature space. Multi-kernel local maximum mean discrepancy is introduced to align subdomains. Moreover, the domain-specific classifiers are applied to diagnose fault category, and diagnosis result is jointly determined by multiple classifiers through weighted decision-making strategy. The experiment results on two sets of rotary machines achieve average accuracy of 98.01% and 96.04%, respectively, which demonstrates the validity of the proposed method.

Keywords

Funding Information

National Key Research and Development Program of China (2022YFF0608700)
National Science Foundation of China (52077064)
Postgraduate Scientific Research Innovation Project of Hunan Province (QL20220079)

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