An Explainable GNN Framework for Component-Level Anomaly Diagnosis

• URL: http://arxiv.org/abs/2608.09246v1
• Date: Mon, 10 Aug 2026 08:08:36 GMT
• Title: An Explainable GNN Framework for Component-Level Anomaly Diagnosis
• Abstract summary: In many industrial systems, anomalies do not arise from faulty sensors but from disruptions in the influences governing the system dynamics.<n>We propose an explainable GNN-based anomaly detection framework that shifts the perspective from sensor-level anomalies to component-level diagnosis.
• Score: 3.4007073989623753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistency [9.032903163326027]
  We propose a novel framework that reframes anomaly detection as the continuous verification of Granger causality.<n>The proposed framework leverages multi-scale alignment to internalize system dynamics.<n>Experiments on real-world industrial datasets demonstrate that the CAAD achieves high-precision anomaly detection.
  arXiv  Detail & Related papers  (2026-07-09T14:46:10Z)
• Can Large Language Models Generate Observability-Aware Code? [51.30941152293301]
  We present a systematic study of observability in agent-generated systems.<n>Our results reveal a consistent gap between diagnostic semantics at the source level and fault signals at runtime.<n>We introduce an observability-oriented skill, which can serve as a guidance to improve both diagnostic semantics and fault-signal exposure.
  arXiv  Detail & Related papers  (2026-07-07T03:20:52Z)
• Reason-IAD: Knowledge-Guided Dynamic Latent Reasoning for Explainable Industrial Anomaly Detection [85.29900916231655]
  Reason-IAD is a knowledge-guided dynamic latent reasoning framework for explainable industrial anomaly detection.<n>Experiments demonstrate that Reason-IAD consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-02-10T14:54:17Z)
• Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications [58.796149594878585]
  This paper proposes a low-power online anomaly detection framework based on neuromorphic wireless sensor networks.<n>In the considered system, a central reader node actively queries a subset of neuromorphic sensor nodes (neuro-SNs) at each time frame.<n>The neuromorphic sensors are event-driven, producing spikes in correspondence to relevant changes in the monitored system.
  arXiv  Detail & Related papers  (2025-10-16T13:56:54Z)
• Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systems [24.640375297580288]
  This paper addresses the challenges of anomaly detection, root cause localization, and anomaly type classification in dynamical systems governed by ordinary differential equations (ODEs)<n>We propose the Interpretable Causality Ordinary Differential Equation (ICODE) Networks, a model-intrinsic explainable learning framework.<n>ICODE is designed to simultaneously perform anomaly detection, root cause analysis (RCA), and anomaly type classification within a single, interpretable framework.
  arXiv  Detail & Related papers  (2025-02-17T18:01:07Z)
• Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties [22.51903015541254]
  MulSen-AD is the first high-resolution, multi-sensor anomaly detection dataset tailored for industrial applications.<n>The dataset spans 15 industrial products with diverse, real-world anomalies.<n>Our experiments demonstrate that multi-sensor fusion substantially outperforms single-sensor approaches.
  arXiv  Detail & Related papers  (2024-12-19T07:23:17Z)
• Explainable Online Unsupervised Anomaly Detection for Cyber-Physical Systems via Causal Discovery from Time Series [1.223779595809275]
  State-of-the-art approaches based on deep learning via neural networks achieve outstanding performance at anomaly recognition. We show that our method has higher training efficiency, outperforms the accuracy of state-of-the-art neural architectures.
  arXiv  Detail & Related papers  (2024-04-15T15:42:12Z)
• Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
  We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism. Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors. To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
  arXiv  Detail & Related papers  (2023-08-06T05:58:45Z)
• DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems [12.641578474466646]
  DyEdgeGAT is a novel approach for early-stage fault detection in IIoT systems. It incorporates operating condition contexts into node dynamics modeling, enhancing its accuracy and robustness. We rigorously evaluated DyEdgeGAT using both a synthetic dataset and a real-world industrial-scale flow facility benchmark.
  arXiv  Detail & Related papers  (2023-07-07T12:22:16Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observers [6.432798111887824]
  Sensor fault detection and isolation (s-FDI) method applies to a general class of nonlinear systems. Key aspect of this approach lies in the utilization of a neural network-based Kazantzis-Kravaris/Luenberger (KKL) observer.
  arXiv  Detail & Related papers  (2023-04-18T09:05:07Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.