Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163780
Title: Conditional contrastive domain generalization for fault diagnosis
Authors: Ragab, Mohamed
Chen, Zhenghua
Zhang, Wenyu
Eldele, Emadeldeen
Wu, Min
Kwoh, Chee Keong
Li, Xiaoli
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: 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, 3506912-. https://dx.doi.org/10.1109/TIM.2022.3154000
Project: A20H6b0151
C210112046
Journal: IEEE Transactions on Instrumentation and Measurement
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 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.
URI: https://hdl.handle.net/10356/163780
ISSN: 0018-9456
DOI: 10.1109/TIM.2022.3154000
DOI (Related Dataset): 10.21979/N9/8QPQHL
Schools: School of Computer Science and Engineering 
Organisations: Institute for Infocomm Research, A*STAR
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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