用于组件级异常诊断的可解释GNN框架
An Explainable GNN Framework for Component-Level Anomaly Diagnosis
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中文总结 AI 辅助
该研究提出一种可解释GNN框架，将异常诊断视角从传感器级转向组件级，通过识别传感器间相互作用改变来定位故障组件，实验验证了其有效性。
中文摘要 AI 辅助
工业过程是由多个相互作用的传感器组成的复杂系统，这些传感器生成多元时间序列（MTS）。检测此类系统中的异常对可靠性和安全性至关重要，但了解异常的起源同样重要。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级偏差，要么直接将异常归因于偏差传感器，要么在尝试诊断时将偏差最大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，而是源于控制系统动态的相互作用出现中断。我们提出一种基于GNN的可解释异常检测框架，该框架将视角从传感器级异常转向组件级诊断，假设异常测量是传感器间相互作用改变的症状。实验表明，该方法能有效识别并排序真正的故障组件，为系统故障提供可解释的见解。
英文摘要
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.
