Learning Informative Health Indicators Through Unsupervised Contrastive Learning

TL;DR

This paper introduces an unsupervised contrastive learning method to develop health indicators for industrial assets, effectively tracking wear and detecting faults across different systems without labeled data.

Contribution

The study presents a novel contrastive learning approach for unsupervised health indicator development applicable to diverse industrial systems and conditions.

Findings

Achieved 0.97 correlation in wear assessment of milling machines.

Attained 88.7% balanced accuracy in railway wheel fault detection.

Demonstrated versatility across different systems and health conditions.

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.