# Classifier-Free Diffusion-Based Weakly-Supervised Approach for Health Indicator Derivation in Rotating Machines: Advancing Early Fault Detection and Condition Monitoring

arXiv: https://arxiv.org/abs/2409.01676 · HTML: https://mlsanity.com/paper/2409.01676/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [Diffusion Self-Guidance for Controllable Image Generation](https://mlsanity.com/paper/2306.00986/) (2023)
- [Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications](https://mlsanity.com/paper/2005.02144/) (2020)
- [Diffusion Models Beat GANs on Image Classification](https://mlsanity.com/paper/2307.08702/) (2023)
- [Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection](https://mlsanity.com/paper/2105.10500/) (2021)
- [An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery](https://mlsanity.com/paper/2102.11848/) (2021)
- [A Comparison of Residual-based Methods on Fault Detection](https://mlsanity.com/paper/2309.02274/) (2023)
- [CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection](https://mlsanity.com/paper/2202.07831/) (2022)
- [Semi-Supervised Health Index Monitoring with Feature Generation and Fusion](https://mlsanity.com/paper/2312.02867/) (2023)
- [Learning Informative Health Indicators Through Unsupervised Contrastive Learning](https://mlsanity.com/paper/2208.13288/) (2022)

## Built on by (most cited first, 1 of 1)

- [Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring](https://mlsanity.com/paper/2608.14666/) (2026)

## Abstract (arXiv)

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference but also lacking the explainability. To address these issues, we propose a diffusion-based weakly-supervised approach for deriving health indicators of rotating machines, enabling early fault detection and continuous monitoring of condition evolution. This approach relies on a classifier-free diffusion model trained using healthy samples and a few anomalies. This model generates healthy samples. and by comparing the differences between the original samples and the generated ones in the envelope spectrum, we construct an anomaly map that clearly identifies faults. Health indicators are then derived, which can explain the fault types and mitigate noise interference. Comparative studies on two cases demonstrate that the proposed method offers superior health monitoring effectiveness and robustness compared to baseline models.