Causal Disentangled Graph Neural Network for Fault Diagnosis of Complex Industrial Process
Ruonan Liu, Quanhu Zhang, Di Lin, Weidong Zhang, Steven X. Ding
Abstract
Graph neural networks (GNNs) are good at capturing the intricate topologies and dependencies among components and are outstanding in fault diagnosis tasks of complex industrial process. Bias substructures consisting of irrelevant sensor signals and noise data are simpler compared to causal substructures consisting of fault signals, and GNNs tend to utilize the letter to quickly achieve low loss. However, spurious correlations in the bias substructures will mislead predictions. To address this issue, this study takes the disentanglement of causal and bias substructures as the key to improve model stability. A causal disentangled GNN (CDGNN) is proposed. First, sensor signals are transformed into graph data employing an attention mechanism to capture the interactions between them. Then, a causal disentanglement learning module is designed to extract causal subgraphs from input graphs. Finally, causal subgraph features from different source machines are aggregated to form a complete graph representation. Experimental results on two complex industrial datasets indicate that CDGNN is an effective and stable method for fault diagnosis.