AI-Assisted PLC/HMI Documentation and System Understanding

I led a high-uncertainty R&D project to determine whether LLMs could reliably generate accurate, maintainable documentation for legacy PLC ladder logic when grounded in real HMI/operator context.

Facing a scale bottleneck—thousands of rungs and tags that are slow and error-prone to interpret manually—I built a pipeline that parses PLC and HMI source code into structured data, links rungs to screens/alarms/tag usage, and uses a RAG workflow to produce consistent rung/routine explanations and system-level “user flow” summaries with traceability back to source logic.

I provided the client with a practical path to convert opaque control code into searchable engineering knowledge that speeds troubleshooting, onboarding, and change work.

Example AI Comments for Legacy PLC Code

Deliverables: Parsing/extraction scripts, structured exports (CSV/JSON), rung/routine documentation tables, tag dictionary, and flow diagrams.
Value Proposition: Reduced engineering time spent reverse-engineering logic and improved confidence during maintenance and modifications.
Commercial Output: Repeatable process and artifacts suitable for ongoing updates as the controls system evolves.
Core Achievement: Demonstrated scalable, source-traceable documentation generation for PLC/HMI projects grounded in HMI context.