Graph convolutional network-based fault diagnosis of chemical process under sample imbalance

Abstract:

To solve the problem of low accuracy of existing fault diagnosis models under imbalanced data distribution caused by insufficiency of fault samples in practical chemical process, a fault diagnosis model based on cost sensitive multireceptive fields spatio-temporal graph attention network (CSMRFSTGAT) is proposed. This model converts the corresponding variable data collected from chemical process into topological graph data through maximum information coefficient (C_MI) weighted calculation. Using the fault diagnosis model of the graph convolutional network (GCN), multireceptive fields graph convolutional module (MRFGCM) and space-time graph attention module (STGAM) are designed. Then, a hybrid margin-aware focal loss function is proposed to impose more penalties on samples which are difficult to recognize. The proposed model is applied to evaluate its diagnostic performance in multiple imbalanced scenarios of the Tennessee Eastman process (TEP) and the three-phase flow (TPF) dataset. The results show that the proposed model achieves the classification precision and F1 score of more than 91% and 92% in the TPF dataset, and meanwhile the classification recall rate and F1 score in the TEP dataset both break through 99%, respectively; It can recognize faults more efficiently compared with the machine learning model, deep learning model and graph deep learning model. The proposed model has excellent generalization performance in dealing with the data imbalance problem, and can effectively realize chemical process fault diagnosis under sample imbalance.