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Domain Knowledge-Guided Contrastive Learning Framework Based on Complementary Views for Fault Diagnosis With Limited Labeled Data
- Source :
- IEEE Transactions on Industrial Informatics; 2024, Vol. 20 Issue: 5 p8055-8063, 9p
- Publication Year :
- 2024
-
Abstract
- Intelligent fault diagnosis has attracted much attention in industrial processes. The difficulty of collecting fault samples and high price of labeling data, has led to a relative scarcity of labeled data for deep learning tasks in the field. To address this gap, we propose a domain knowledge-guided contrastive learning framework based on complementary data views for fault diagnosis with limited data. Seven data views of either time- or frequency-domains are introduced and designed first. Then, the framework extracts task-specific features by 1) considering complementary information provided by multiple data views to each other, and 2) embedding a domain knowledge-involved space as the guide for the learning process. The results on two bearing datasets show the proposed framework can produce diagnosis accuracies of 96.60% and 94.24% when just 5% of samples have labels. This study determines two pairs of complementary data views that can boost the performance of the proposed framework.
Details
- Language :
- English
- ISSN :
- 15513203
- Volume :
- 20
- Issue :
- 5
- Database :
- Supplemental Index
- Journal :
- IEEE Transactions on Industrial Informatics
- Publication Type :
- Periodical
- Accession number :
- ejs66332522
- Full Text :
- https://doi.org/10.1109/TII.2024.3369704