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

TL;DR

This paper introduces a multi-level temporal graph network with local-global feature fusion for improved industrial fault diagnosis, capturing complex sensor relationships and higher-level fault patterns.

Contribution

It proposes a novel structure-aware multi-level temporal graph network that dynamically constructs correlation graphs and fuses local-global features for better fault detection.

Findings

Achieves superior fault diagnosis performance on Tennessee Eastman process data.

Effectively captures multi-level sensor relationships and higher-level fault patterns.

Outperforms baseline methods in complex fault scenarios.

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.