Complexity of PLC code interpretation and human dependency
PLC code varies widely in structure and syntax across manufacturers, with limited public data available for training or reference. Automated interpretation was further constrained by the need to reference process-specific documents such as communication specifications and vision protocols. As a result, code review and verification relied heavily on specialized PLC engineers. However, field operations required non-specialist personnel within process technology teams to understand and review PLC logic—creating the need for an intelligent, automated interpretation solution.
Automating PLC code analysis with LLM-based AI agents and RAG
An AI solution powered by LLM-based agents was developed to analyze PLC code structures and control logic in natural language. To address manufacturer-specific variations, specialized utilities—such as a command definition explorer and a domain document explorer—were built to enable accurate interpretation of complex logic. A Retrieval-Augmented Generation (RAG) framework ensured continuous updates and efficient data management, while PLC code was parsed into instruction list (mnemonic) format to optimize processing stability and input handling. Together, these capabilities established a scalable and reliable foundation for PLC code interpretation.
Achieving 94% interpretation accuracy
The model achieved 94% interpretation accuracy on validation datasets and was successfully deployed to the client’s internal platform. This enabled the client to independently manage tasks previously dependent on external partners, reducing costs and improving efficiency. A dedicated PLC library and standardized code syntax now support scalability across process lines, production sites, and equipment from multiple manufacturers.
