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.

翻译：本文提出了一种新颖的仅依赖健康数据的可解释状态监测方法。鉴于故障属于罕见事件，我们建议重点学习健康观测数据的概率分布，并在运行时检测异常。该目标通过定义偏离正常状态的概率度量来实现，从而能够检测并预测故障。支撑本方法的贝叶斯视角使我们能够执行不确定性量化以辅助决策。同时，我们提供了增强结果可解释性的描述性工具，以支持所提策略在安全关键型应用中的部署。该方法通过两个用例进行实验验证：一个公开可用的预测性维护基准数据集，以及一个多年收集的真实直升机传动系统数据集。在两项应用中，相较于最先进的异常检测方法，本方法均取得了具有竞争力的检测性能。