Multi-Level Temporal Graph Networks with Local-Global Fusion for Industrial Fault Diagnosis

• URL: http://arxiv.org/abs/2604.18765v1
• Date: Mon, 20 Apr 2026 19:20:29 GMT
• Title: Multi-Level Temporal Graph Networks with Local-Global Fusion for Industrial Fault Diagnosis
• Abstract summary: We propose a structure-aware multi-level temporal graph network with local-global feature fusion for industrial fault diagnosis.<n>The proposed model achieves superior fault diagnosis performance, particularly for complex fault scenarios.
• Score: 0.4779196219827507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fault detection and diagnosis are critical for the optimal and safe operation of industrial processes. The correlations among sensors often display non-Euclidean structures where graph neural networks (GNNs) are widely used therein. However, for large-scale systems, local, global, and dynamic relations extensively exist among sensors, and traditional GNNs often overlook such complex and multi-level structures for various problems including the fault diagnosis. To address this issue, we propose a structure-aware multi-level temporal graph network with local-global feature fusion for industrial fault diagnosis. First, a correlation graph is dynamically constructed using Pearson correlation coefficients to capture relationships among process variables. Then, temporal features are extracted through long short-term memory (LSTM)-based encoder, whereas the spatial dependencies among sensors are learned by graph convolution layers. A multi-level pooling mechanism is used to gradually coarsen and learn meaningful graph structures, to capture higher-level patterns while keeping important fault related details. Finally, a fusion step is applied to combine both detailed local features and overall global patterns before the final prediction. Experimental evaluations on the Tennessee Eastman process (TEP) demonstrate that the proposed model achieves superior fault diagnosis performance, particularly for complex fault scenarios, outperforming various baseline methods.

Related papers

• Domain-Prior-Regularized Graph Modeling for Anomaly Detection in Cyber-Physical Systems [4.42333974179549]
  Anomaly detection is critical for industrial monitoring of cyber-physical systems.<n>Recent graph-based approaches have made significant progress, but they often struggle in small-scale physical systems.<n>We propose DPR-GM, a forecasting-based framework that incorporates system design knowledge into graph construction.
  arXiv  Detail & Related papers  (2026-07-25T13:21:56Z)
• Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes [9.834635805575582]
  We propose a knowledge-assisted multi-graph framework for modeling sensor dependencies in multi-stage industrial processes.<n>We employ a multi-graph attention network, enabling a more accurate and robust representation of complex dependencies.
  arXiv  Detail & Related papers  (2026-07-17T10:10:46Z)
• OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection [48.77471686671269]
  OWLEYE is a novel framework that learns transferable patterns of normal behavior from multiple graphs.<n>We show that OWLEYE achieves superior performance and generalizability compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-01-27T02:08:18Z)
• Graph Neural AI with Temporal Dynamics for Comprehensive Anomaly Detection in Microservices [7.957284443727372]
  This study addresses the problem of anomaly detection and root cause tracing in microservice architectures.<n>It proposes a unified framework that combines graph neural networks with temporal modeling.
  arXiv  Detail & Related papers  (2025-11-05T08:28:41Z)
• Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? [3.3601815104322377]
  Graph Neural Networks (GNNs) are widely adopted for fault diagnosis in microservice systems, premised on their ability to model service dependencies.<n>To isolate the true value of GNNs, we propose DiagMLP, a deliberately minimal, topology-agnostic baseline.<n>DiagMLP achieves performance parity with state-of-the-art GNN-based methods in fault detection, localization, and classification.
  arXiv  Detail & Related papers  (2025-01-06T05:18:13Z)
• Hypergraph-based multi-scale spatio-temporal graph convolution network for Time-Series anomaly detection [8.878898677348086]
  Multi-dimensional time series anomaly detection technology plays an important role in many fields including aerospace, water treatment, cloud service providers, etc. It is becoming increasingly challenging to perform effective and accurate anomaly detection in high-dimensional and complex data sets. We propose a hypergraph basedtemporal graph convolutional network model STGCN_Hyper, which explicitly captures high-order, multi-hop correlations between multiple variables. Our model can flexibly learn the multi-scale time series features in the data and the dependencies between features, and outperforms most existing baseline models in terms of precision, recall, F1-score on anomaly detection
  arXiv  Detail & Related papers  (2024-10-29T17:19:18Z)
• Spatial-Temporal Bearing Fault Detection Using Graph Attention Networks and LSTM [0.7864304771129751]
  This paper introduces a novel method that combines Graph Attention Network (GAT) and Long Short-Term Memory (LSTM) networks. This approach captures both spatial and temporal dependencies within sensor data, improving the accuracy of bearing fault detection.
  arXiv  Detail & Related papers  (2024-10-15T12:55:57Z)
• Online Multi-modal Root Cause Analysis [61.94987309148539]
  Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems. Existing online RCA methods handle only single-modal data overlooking, complex interactions in multi-modal systems. We introduce OCEAN, a novel online multi-modal causal structure learning method for root cause localization.
  arXiv  Detail & Related papers  (2024-10-13T21:47:36Z)
• Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality [39.476378833827184]
  Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. We introduce a novel spatial- temporal memories-enhanced graph autoencoder (STRIPE) STRIPE significantly outperforms existing methods with 5.8% improvement in AUC scores and 4.62X faster in training time.
  arXiv  Detail & Related papers  (2024-03-14T02:26:10Z)
• GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection [49.9884374409624]
  GLAD is a Graph-based Log Anomaly Detection framework designed to detect anomalies in system logs. We introduce GLAD, a Graph-based Log Anomaly Detection framework designed to detect anomalies in system logs.
  arXiv  Detail & Related papers  (2023-09-12T04:21:30Z)
• Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection [67.60791405198063]
  We propose a correlation-aware spatial-temporal graph learning (termed CST-GL) for time series anomaly detection. CST-GL explicitly captures the pairwise correlations via a multivariate time series correlation learning module. A novel anomaly scoring component is further integrated into CST-GL to estimate the degree of an anomaly in a purely unsupervised manner.
  arXiv  Detail & Related papers  (2023-07-17T11:04:27Z)
• Coupled Attention Networks for Multivariate Time Series Anomaly Detection [10.620044922371177]
  We propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data. To capture inter-sensor relationships and temporal dependencies, a convolutional neural network based on the global-local graph is integrated with a temporal self-attention module.
  arXiv  Detail & Related papers  (2023-06-12T13:42:56Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• Consistency of mechanistic causal discovery in continuous-time using Neural ODEs [85.7910042199734]
  We consider causal discovery in continuous-time for the study of dynamical systems. We propose a causal discovery algorithm based on penalized Neural ODEs.
  arXiv  Detail & Related papers  (2021-05-06T08:48:02Z)

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.