Graph neural network-based method for classifying continuous lines in piping and instrumentation diagram

초록

Due to the limited usability of unstructured piping and instrumentation diagrams (P&IDs) in plant projects, most companies manually digitize them into digital P&IDs. To automate the digitization of P&IDs, this study proposes a graph neural network (GNN)-based method to classify detected continuous lines. We present the graph structure that represents the connections of continuous lines and the process of generating these graphs. Additionally, we introduce the continuous line classification network (ContLineNet), which classifies the nodes of the continuous line connection graph into eight classes. Experiments were conducted using P&IDs from three different companies. P&IDs containing 7,371 continuous lines from one specific company were used to train ContLineNet. The trained ContLineNet achieved average values of 96.968% for precision, 96.778% for recall, and 96.700% for the F1 score on 1,924 continuous lines across five P&IDs from three companies. These experimental results demonstrate the applicability of our method to various data.

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