A Graph‐Based Generative Artificial Intelligence Methodology for Autocorrection of Utility‐System P&IDs

ABSTRACT Piping and instrumentation diagrams (P&IDs) are central engineering documents whose review remains largely manual, time‐consuming and error‐prone. We investigate generative artificial intelligence (GenAI) for P&ID revision. Specifically, we interpret P&ID correction as a machine translation task. Machine‐readable DEXPI P&IDs are converted with pyDEXPI into attributed graphs, and corrected topologies are represented as generalized SFILES sequences (GGILES). Based on this representation, we adapt a transformer‐based Graph‐to‐SFILES model to utility‐system P&IDs and train it on a synthetic dataset of paired erroneous and corrected diagrams. On this benchmark, the model achieves high accuracy and learns attribute‐dependent error patterns. Tests on five industrial DEXPI P&IDs reveal a gap between synthetic and industrial data, highlighting limitations in data availability and coverage.