Conditional Contrastive Domain Generalization for Fault Diagnosis

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Conditional Contrastive Domain Generalization for Fault Diagnosis
Title:
Conditional Contrastive Domain Generalization for Fault Diagnosis
Other Titles:
IEEE Transactions on Instrumentation and Measurement
Keywords:
Publication Date:
24 February 2022
Citation:
Ragab, M., Chen, Z., Zhang, W., Eldele, E., Wu, M., Kwoh, C.-K., & Li, X. (2022). Conditional Contrastive Domain Generalization for Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 71, 1–12. https://doi.org/10.1109/tim.2022.3154000
Abstract:
Data-driven fault diagnosis plays a key role in stability and reliability of operations in modern industries. Recently, deep learning has achieved remarkable performance in fault classification tasks. However, in reality, the model can be deployed under highly varying working environments. As a result, the model trained under a certain working environment (i.e., certain distribution) can fail to generalize well on data from different working environments (i.e., different distributions). The naive approach of training a new model for each new working environment would be infeasible in practice. To address this issue, we propose a novel conditional contrastive domain generalization (CCDG) approach for fault diagnosis of rolling machinery, which is able to capture shareable class-information and learn environment-independent representation among data collected from different environments (also known as domains). Specifically, our CCDG attempts to maximize the mutual information of similar classes across different domains while minimizing mutual information among different classes, such that it can learn domain-independent class representation that can be transferable to new unseen domains. Our proposed approach significantly outperforms state-of-the-art methods on two real-world fault diagnosis datasets with an average improvement 7.75% and 2.60% respectively. The promising performance of our proposed CCDG on new unseen target domain contributes towards more practical data-driven approaches that can work under challenging real-world environments.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AME Programmatic Funds
Grant Reference no. : A20H6b0151

This research / project is supported by the A*STAR - Career Development Award
Grant Reference no. : C210112046
Description:
© 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.
ISSN:
0018-9456
1557-9662
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