用扩散模型从少量异常数据中提取旋转机械健康指标，提升故障检测精度。
Classifier-Free Diffusion-Based Weakly-Supervised Approach for Health Indicator Derivation in Rotating Machines: Advancing Early Fault Detection and Condition Monitoring
- 基于无分类器扩散模型，仅需正常样本和少量异常样本训练
- 通过包络谱对比生成与原始样本差异，构建清晰故障图谱
- 适合工业设备早期故障诊断，对噪声不敏感，结果可解释
旋转机械的健康指标提取对其维护至关重要。然而，现有智能方法常依赖完整数据分布，不仅引入噪声干扰，还缺乏可解释性。为此，本文提出一种基于无分类器扩散模型的弱监督方法，用于旋转机械健康指标提取，实现早期故障检测与状态演化连续监控。该方法仅使用正常样本和少量异常样本训练扩散模型，生成正常样本；通过比较原始样本与生成样本在包络谱上的差异，构建故障图谱，从而提取健康指标。该指标可解释故障类型，并有效抑制噪声干扰。在两个案例上的对比实验表明，该方法在健康监测效果与鲁棒性方面均优于基线模型。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。