仅用正常数据学习分布，实时检测直升机传动异常并解释结果。
Explainable Condition Monitoring via Probabilistic Anomaly Detection Applied to Helicopter Transmissions
- 基于健康数据构建概率模型，通过偏离度判断异常
- 在两个真实数据集上达到领先检测效果
- 支持安全场景应用，结果可解释性强
我们提出一种新型可解释的故障监测方法，仅依赖正常运行数据。由于故障发生稀少，该方法聚焦于学习正常状态的概率分布，并在运行时检测异常。通过定义概率化的偏离度量，实现故障的提前发现与预警。该方法基于贝叶斯框架，可进行不确定性量化以辅助决策；同时提供可视化工具增强结果可解释性，适用于安全关键场景。在两个实际案例中验证：一个公开的预测性维护基准数据集，以及多年采集的真实直升机传动系统数据。实验表明，该方法在检测性能上优于当前主流异常检测技术。
原文摘要 · Abstract (English)
We present a novel Explainable methodology for Condition Monitoring, relying on healthy data only. Since faults are rare events, we propose to focus on learning the probability distribution of healthy observations only, and detect Anomalies at runtime. This objective is achieved via the definition of probabilistic measures of deviation from nominality, which allow to detect and anticipate faults. The Bayesian perspective underpinning our approach allows us to perform Uncertainty Quantification to inform decisions. At the same time, we provide descriptive tools to enhance the interpretability of the results, supporting the deployment of the proposed strategy also in safety-critical applications. The methodology is validated experimentally on two use cases: a publicly available benchmark for Predictive Maintenance, and a real-world Helicopter Transmission dataset collected over multiple years. In both applications, the method achieves competitive detection performance with respect to state-of-the-art anomaly detection methods.
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