2026-09-28T11:48:18Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:27816 2026-09-28T06:35:12Z ddc:004 open_access

An Explainable GNN Framework for Component-Level Anomaly Diagnosis Ozgunay, Sena Travé-Massuyès, Louise Loubes, Jean-Michel Ferreira, Raul Sena Anomaly Detection Fault Diagnosis Graph Neural Networks Time Series 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Sena Ozgunay and Louise Travé-Massuyès and Jean-Michel Loubes and Raul Sena Ferreira 2026 Is Part Of OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.DX.2026.2 urn:nbn:de:0030-drops-278163 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.2 eng https://creativecommons.org/licenses/by/4.0/legalcode