Causal intervention graph neural network for fault diagnosis of complex industrial processes

With the development of industry and manufacturing, the mechanical structures of equipment have become intricate and complex. Due to the interaction between components, once a failure occurs, it will propagate through the industrial processes, resulting in multiple sensor anomalies. Identifying the root causes of faults and eliminating interference from irrelevant sensor signals are critical issues in enhancing the stability and reliability of intelligent fault diagnosis. The components of industrial processes and their interactions can be represented by a structural attribute graph. The causal subgraph formed by fault signals determines the fault mode, while irrelevant sensor signals constitute a non-causal subgraph. The structure of non-causal subgraphs is relatively simple, and graph neural networks tend to use this part as a shortcut for prediction, leading to a significant decrease in prediction accuracy. To address this issue, a causal intervention graph neural network (CIGNN) framework is proposed. First, the sensor signals are constructed into structural attribute graphs using an attention mechanism. Due to causal and confounding features are highly coupled in graphs, explicitly decoupling them is almost impossible. Then, we design an instrumental variable to implement causal intervention to mitigate the confounding effect. Experimental results on two complex industrial datasets demonstrate the reliability and effectiveness of the proposed method in fault diagnosis.