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

TL;DR

This paper introduces a diffusion-based weakly-supervised method for health indicator derivation in rotating machines, improving early fault detection and condition monitoring by generating healthy samples and identifying anomalies.

Contribution

It presents a novel classifier-free diffusion model trained on healthy and few anomalous samples, enhancing fault detection and explainability in machine health monitoring.

Findings

Outperforms baseline models in health monitoring accuracy

Provides clear fault identification through anomaly maps

Enhances robustness against noise interference

Abstract

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.