Debiased Contrastive Learning With Supervision Guidance for Industrial Fault Detection

• Published in: IEEE transactions on industrial informatics pp. 1 - 12
• Main Authors: Cai, Rongyao, Gao, Wang, Peng, Linpeng, Lu, Zhengming, Zhang, Kexin, Liu, Yong
• Format: Journal Article
• Language: English
• Published: IEEE 18.07.2024
• Subjects: Contrastive learning; Data augmentation; debiased contrastive learning; Fault detection; Feature extraction; Informatics; Measurement; representation extraction; similarity metric; Task analysis; Time series analysis; weak supervision guidance
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2024.3424561

The time series self-supervised contrastive learning framework has succeeded significantly in industrial fault detection scenarios. It typically consists of pretraining on abundant unlabeled data and fine-tuning on limited annotated data. However, the two-phase framework faces three challenges: Sampling bias, task-agnostic representation issue, and angular-centricity issue. These challenges hinder further development in industrial applications. This article introduces a debiased contrastive learning with supervision guidance (DCLSG) framework and applies it to industrial fault detection tasks. First, DCLSG employs channel augmentation to integrate temporal and frequency domain information. Pseudolabels based on momentum clustering operation are assigned to extracted representations, thereby mitigating the sampling bias raised by the selection of positive pairs. Second, the generated supervisory signal guides the pretraining phase, tackling the task-agnostic representation issue. Third, the angular-centricity issue is addressed using the proposed Gaussian distance metric measuring the radial distribution of representations. The experiments conducted on three industrial datasets (ISDB, CWRU, and practical datasets) validate the superior performance of the DCLSG compared to other fault detection methods.