Class-Imbalance Adversarial Transfer Learning Network for Cross-Domain Fault Diagnosis With Imbalanced Data

Recently, cross-domain fault diagnosis based on transfer learning methods has been extensively explored and well-addressed when class-balance data with supervision information are available. However, data under machine faulty states are mostly difficult to collect; there is a huge divide between cur...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2022, Vol.71, p.1-11
Main Authors: Kuang, Jiachen, Xu, Guanghua, Tao, Tangfei, Wu, Qingqiang
Format: Article
Language:English
Subjects:
Adversarial training
class-imbalance learning
Classification algorithms
Domains
Fault diagnosis
Feature extraction
Gearboxes
Generative adversarial networks
imbalanced data
Industrial applications
intelligent fault diagnosis
Invariants
Learning
Task analysis
Training
Transfer learning
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Summary:Recently, cross-domain fault diagnosis based on transfer learning methods has been extensively explored and well-addressed when class-balance data with supervision information are available. However, data under machine faulty states are mostly difficult to collect; there is a huge divide between current transfer learning methods based on implicit class-balance data and real industrial applications. In this article, we propose a class-imbalance adversarial transfer learning (CIATL) network with input being imbalanced data to learn domain-invariant and knowledge. Within this framework, class-imbalance learning is embedded into the adversarial training process to learn class-separate diagnostic knowledge with imbalanced data, double-level adversarial transfer learning including marginal and conditional distribution adaptations is conducted to learn domain-invariant knowledge. Extensive experiments on a planetary gearbox rig with imbalanced data verify the effectiveness and generalization of the proposed method and show its superior performance over contrastive transfer learning methods. Moreover, the proposed method relaxes the underlying assumption that the success of current transfer learning regimes is rooted in class-balance data and extends the application of the transfer learning method for real-industrial cross-domain diagnosis tasks.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2021.3136175