Condition monitoring is essential to operate industrial assets safely and
efficiently. To achieve this goal, the development of robust health indicators
has recently attracted significant attention. These indicators, which provide
quantitative real-time insights into the health status of industrial assets
over time, serve as valuable tools for fault detection and prognostics. In this
study, we propose a novel and universal approach to learn health indicators
based on unsupervised contrastive learning. Operational time acts as a proxy
for the asset's degradation state, enabling the learning of a contrastive
feature space that facilitates the construction of a health indicator by
measuring the distance to the healthy condition. To highlight the universality
of the proposed approach, we assess the proposed contrastive learning framework
in two distinct tasks - wear assessment and fault detection - across two
different case studies: a milling machines case study and a real condition
monitoring case study of railway wheels from operating trains. First, we
evaluate if the health indicator is able to learn the real health condition on
a milling machine case study where the ground truth wear condition is
continuously measured. Second, we apply the proposed method on a real case
study of railway wheels where the ground truth health condition is not known.
Here, we evaluate the suitability of the learned health indicator for fault
detection of railway wheel defects. Our results demonstrate that the proposed
approach is able to learn the ground truth health evolution of milling machines
and the learned health indicator is suited for fault detection of railway
wheels operated under various operating conditions by outperforming
state-of-the-art methods. Further, we demonstrate that our proposed approach is
universally applicable to different systems and different health conditions