Causal-Trivial Attention Graph Neural Network for Fault Diagnosis of Complex Industrial Processes

In modern industrial systems, components have complex interactions with each other, which makes it become a challenging task to identify the operational conditions of industrial systems. Considering that an industrial system, the embedded components and their interactions can be expressed as nodes and edges in a graph, respectively. Therefore, graph representation algorithms are powerful tools for fault diagnosis of industrial systems. As one of the most commonly used graph representation algorithms, graph neural networks (GNN) mainly follow the law of "learning to attend." GNN extract training data features learn the statistical correlations between features and labels, resulting in the attended graph favoring for accessing noncausal features as a shortcut for prediction. This shortcut feature is unstable and depends on the data distribution characteristics in the training dataset, which reduces the generalization ability of the classifier. By performing the causal analysis of GNN modeling for graph representation, the results show that shortcut features act as confounding factors between causal features and predictions, causing classifiers to learn wrong correlations. Therefore, to discover patterns of causality and weaken the confounding effects of shortcut features, a causal-trivial attention graph neural network strategy is proposed. First, node and edge representations are given by estimating soft masks. Second, through disentanglement, both causal features and shortcut features are obtained from the graph. Third, the backdoor adjustment of the causal theory is parameterized to combine each causal feature with a variety of shortcut features. Finally, comparative experiments on the three-phase flow facility dataset illustrate the effectiveness of the proposed method.