用图神经网络从传感器关联变化诊断设备部件故障
An Explainable GNN Framework for Component-Level Anomaly Diagnosis
- 通过分析传感器间动态影响关系，而非仅看传感器数值异常
- 在真实工业数据上准确定位故障部件，优于传统方法
- 适合需要可解释性故障诊断的智能制造场景
工业过程是由多个相互作用的传感器构成的复杂系统，产生多变量时间序列（MTS）。检测此类系统中的异常对可靠性和安全性至关重要，但理解其根源同样重要。现有的基于图神经网络（GNN）的异常检测方法主要关注传感器层面的偏差，并将异常直接归因于偏离的传感器。当进行诊断时，通常将最偏离的传感器识别为系统故障的根本原因。然而，在许多工业系统中，异常并非源于故障传感器，而是源于影响系统动态的相互作用发生改变。我们提出一种可解释的GNN异常检测框架，将视角从传感器级异常转向部件级诊断，假设异常测量是传感器间影响发生变化的症状。实验表明，该方法能有效识别并优先排序真正故障的部件，为系统故障提供可解释的洞察。
原文摘要 · Abstract (English)
Industrial processes are complex systems composed of multiple interacting sensors that generate multivariate time series (MTS). Detecting anomalies in such systems is critical for reliability and safety, yet understanding their origin is equally important. Existing Graph Neural Network (GNN)based methods for anomaly detection primarily focus on sensor-level deviations and either attribute anomalies directly to the deviating sensors. When diagnosis is attempted, generally, the most deviated sensor is identified as a root cause of a system fault. However, in many industrial systems, anomalies do not arise from faulty sensors but from disruptions in the influences governing the system dynamics. We propose an explainable GNN-based anomaly detection framework that shifts the perspective from sensor-level anomalies to component-level diagnosis, hypothesizing that anomalous measurements are symptoms of altered inter-sensor influences. Experiments show that the method effectively identifies and prioritizes the true faulty components, providing interpretable insights into system failures.
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