Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 71, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2022.3154000
Keywords
Fault diagnosis; Task analysis; Feature extraction; Mutual information; Machinery; Training; Instruments; Contrasting learning; domain generalization (DG); intelligent fault diagnosis; mutual information
Funding
- Agency for Science, Technology and Research (A*STAR) through its AME Programmatic Funds [A20H6b0151]
- Career Development Award [C210112046]
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Data-driven fault diagnosis is crucial in modern industries, and our proposed CCDG approach effectively improves the model's generalization capability under different working environments. It achieves remarkable performance in fault diagnosis of rolling machinery, outperforming state-of-the-art methods on real-world datasets.
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 of 7.75% and 2.60%, respectively. The promising performance of our proposed CCDG on new unseen target domain contributes toward more practical data-driven approaches that can work under challenging real-world environments.
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