D.P. Goldstein
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Journal article
(2026)
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L. Schulze Balhorn, D.P. Goldstein, Niels Seijsener, Kevin Dao, Ge H.M. Driessen, A.M. Schweidtmann
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.
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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.