# Learning Informative Health Indicators Through Unsupervised Contrastive Learning

arXiv: https://arxiv.org/abs/2208.13288 · HTML: https://mlsanity.com/paper/2208.13288/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 28 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Unsupervised Scalable Representation Learning for Multivariate Time Series](https://mlsanity.com/paper/1901.10738/) (2019)
- [Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications](https://mlsanity.com/paper/2005.02144/) (2020)
- [Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series](https://mlsanity.com/paper/2105.00899/) (2021)
- [Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder](https://mlsanity.com/paper/1608.06154/) (2016)
- [Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals](https://mlsanity.com/paper/1810.05550/) (2018)

## Built on by (most cited first, 3 of 3)

- [I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation](https://mlsanity.com/paper/2511.21208/) (2025)
- [Classifier-Free Diffusion-Based Weakly-Supervised Approach for Health Indicator Derivation in Rotating Machines: Advancing Early Fault Detection and Condition Monitoring](https://mlsanity.com/paper/2409.01676/) (2024)
- [STEP: Learning STructured Embeddings for Progressive Time Series](https://mlsanity.com/paper/2605.31061/) (2026)

## Abstract (arXiv)

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.