Pruning graph convolutional network-based feature learning for fault diagnosis of industrial processes

In recent years, deep learning has been widely applied in process fault diagnosis due to its powerful feature extraction ability. A predominant property of these fault diagnosis models is to extract effective features from process signal. However, it is still difficult for them to construct the feature association relationship between input data. To solve these problems, this paper proposes a new graph neural network (GNN), pruning graph Convolutional network (PGCN), to perform feature learning based on the graph data. One dimensional process data are transformed into graph data by a graph construction method. A graph Convolutional network (GCN) is used to extract the features of process data. A pruning method of graph structure is proposed to effectively extract important information from process fault data. The feasibility and effectiveness of PGCN are verified on two benchmark processes, i.e., continuous stirred-tank reactor (CSTR) and fed-batch fermentation penicillin process (FBFP). The experimental results show that the performance of PGCN in feature extraction and process fault diagnosis is better than that of other typical methods, which provides a good possibility for the application of GCN in industrial process fault diagnosis.