Learning Informative Health Indicators Through Unsupervised Contrastive Learning

• Main Authors: Rombach, Katharina, Michau, Gabriel, Bürzle, Wilfried, Koller, Stefan, Fink, Olga
• Format: Journal Article
• Language: English
• Published: 28-08-2022
• Subjects: Computer Science - Learning
• DOI: 10.48550/arxiv.2208.13288

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.