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An Explainable GNN Framework for Component-Level Anomaly Diagnosis

Authors Sena Ozgunay , Louise Travé-Massuyès , Jean-Michel Loubes , Raul Sena Ferreira

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Subject Classification

ACM Subject Classification
  • Computing methodologies
Keywords
  • Anomaly Detection
  • Fault Diagnosis
  • Graph Neural Networks
  • Time Series

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Abstract

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.

Cite As Get BibTex

Sena Ozgunay, Louise Travé-Massuyès, Jean-Michel Loubes, and Raul Sena Ferreira. An Explainable GNN Framework for Component-Level Anomaly Diagnosis. In 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026). Open Access Series in Informatics (OASIcs), Volume 148, pp. 2:1-2:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/OASIcs.DX.2026.2

Author Details

Sena Ozgunay
  • LAAS-CNRS, Université de Toulouse, France
Louise Travé-Massuyès
  • LAAS-CNRS, Université de Toulouse, France
Jean-Michel Loubes
  • INRIA, Toulouse, France
Raul Sena Ferreira
  • Airbus Protect, Toulouse, France

Funding

This work is supported by the AI Interdisciplinary Institute ANITI, funded by the France 2030 program under the Grant agreements n° ANR-19-PI3A-0004 and n° ANR-23-IACL-0002.

Acknowledgements

The authors would also like to thank Ferhat Tamssaouet for valuable support and insightful discussions for this work.

Supplementary Materials

References

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