Causal Counterfactual Faithfulness Generation for Open-Set Fault Diagnosis of Complex Industrial Processes

Ruonan Liu, Puyuan Hu, Siheng Zhao, Di Lin, Weidong Zhang, Steven X. Ding

Abstract: Traditional intelligent fault diagnosis models are usually capable of diagnosing known types of faults. However, in the field of industrial fault diagnosis in open environments, it is almost impossible to collect training samples that cover all fault categories. Therefore, when encountering unknown types of fault, traditional methods tend to misclassify them as known categories. To address this issue, a causal counterfactual faithfulness generation method is proposed for open-set fault diagnosis of complex industrial processes. Initially, the signal data from fault sensors are processed into graph data composed of nodes and edges. Then, the features of nodes and their adjacent nodes are learned and integrated into graph architecture to generate new fault sample attributes. Subsequently, the causal generative model infers the category features and combines known fault categories to generate counterfactual samples. Finally, the sample’s classification as an unknown category is ultimately determined by testing the principle of consistency. The proposed method can significantly improve the accuracy of open-set diagnosis without affecting the accuracy of closed-set classification. Comparison experiments with multiple baseline models in two fault datasets illustrated that the proposed method shows an improvement in almost all indicators, which ultimately verified the effectiveness of the proposed method in the task of fault diagnosis in open environments.

External IDs:dblp:journals/tii/LiuHZLZD25