Enhancing consistency in piping and instrumentation diagrams using DistilBERT and smart PID systems

This study presents a novel approach utilizing DistilBERT, a lightweight variant of BERT, to identify inconsistencies in piping and instrumentation diagrams (P&IDs) within SmartPID systems. A structured dataset was constructed by extracting engineering design data from a SQL-based SmartPID database, monitoring all modifications and updates made throughout the design phase. The DistilBERT model was fine-tuned on this dataset to recognize inconsistencies in real-time, achieving an impressive F1 score of 99% and a loss of 0.04%. The model’s performance was validated by domain experts, who confirmed the detected inconsistencies as highly accurate. Our approach significantly reduces the manual effort required for P&ID review and improves design consistency, demonstrating the potential for enhanced safety and efficiency in complex industrial projects. Future work will focus on refining the model’s parameters and expanding its application across different industries. • DistilBERT Application for Inconsistency Detection: The study successfully trained a DistilBERT model to detect inconsistencies in Smart P&ID databases with remarkable results, achieving an F1 score of 99% and a loss of just 0.04%. The model was specifically tuned for the intelligent engineering design process, aiming to minimize human error in reviewing P&IDs by automating inconsistency detection. • Dataset Construction and Views: The research involved building a dataset by tracking changes within the Smart P&ID system. Views were constructed to focus on unresolved inconsistencies and relationships between engineering elements, which provided the necessary labeled data for model training. This data structure allowed the model to identify and resolve inconsistencies in engineering designs. • Scalability and Performance of DistilBERT: The model was able to handle large datasets efficiently, with training experiments demonstrating rapid convergence and high performance. The research highlights the model’s potential for scalability, suggesting that it can generalize well to complex datasets beyond the initial scope of this project, which points to future applications in other industrial contexts.