Multichannel Dynamic Graph Convolutional Network-Based Fault Diagnosis and Its Application in Blast Furnace Ironmaking Process

To ensure the operation safety of complex industrial processes, faults that occur in the process should be detected and identified in time to avoid further catastrophic events. Therefore, fault diagnosis plays an indispensable role in the process industry. With the expansion of the production scale of industrial processes, the structural attributed graph is suitable for describing the process data structure due to the complicated interactions between sensor measurements. Graph convolutional networks (GCNs) can identify and capture the relationships between industrial process data in non-Euclidean space by taking graph data with topological structure as input. In this article, a novel fault diagnosis method based on a multichannel dynamic GCN (MDGCN) is proposed. Different from traditional GCNs, the proposed MDGCN assigns different weights to the nodes of the graph. Thus, more useful information about process dynamics is extracted. Particularly, the strategy of multiple isomorphic graph channels is developed to learn feature representations of process data from different levels for fault diagnosis. The capability and efficiency of the proposed MDGCN-based fault diagnosis method are demonstrated through an industrial benchmark of the Tennessee Eastman process (TEP) and a real blast furnace iron-making process (BFIP).