Learning Informative Health Indicators Through Unsupervised Contrastive Learning
- URL: http://arxiv.org/abs/2208.13288v3
- Date: Tue, 28 May 2024 08:03:29 GMT
- Title: Learning Informative Health Indicators Through Unsupervised Contrastive Learning
- Authors: Katharina Rombach, Gabriel Michau, Wilfried Bürzle, Stefan Koller, Olga Fink,
- Abstract summary: This study proposes a novel, versatile and unsupervised approach to learn health indicators.
The approach is evaluated on two tasks and case studies with different characteristics.
Our results show that the proposed methodology effectively learns a health indicator that follows the wear of milling machines.
- Score: 5.193936395510582
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Monitoring the health of complex industrial assets is crucial for safe and efficient operations. Health indicators that provide quantitative real-time insights into the health status of industrial assets over time serve as valuable tools for e.g. fault detection or prognostics. This study proposes a novel, versatile and unsupervised approach to learn health indicators using contrastive learning, where the operational time serves as a proxy for degradation. To highlight its versatility, the approach is evaluated on two tasks and case studies with different characteristics: wear assessment of milling machines and fault detection of railway wheels. Our results show that the proposed methodology effectively learns a health indicator that follows the wear of milling machines (0.97 correlation on average) and is suitable for fault detection in railway wheels (88.7% balanced accuracy). The conducted experiments demonstrate the versatility of the approach for various systems and health conditions.
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