Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models
Engineering specifications such as interlocks, alarm rationalization tables, and cause-and-effect (C&E) matrices remain central to process control and safety, yet their creation is still predominantly manual, document-driven, and prone to inconsistency. This paper presents a semantic-AI framework that automates the generation of C&E logic by combining a knowledge graph (KG) with a constrained large language model (LLM) layer. The KG builds on an established modular alignment ontology to represent process structure, operating modes, faults, symptoms, causes, and mitigation actions in a machine-interpretable form. The LLM then transforms this information into operator-ready safety narrative...
| Authors | Javal Vyas, Milapji Singh Gill, Mehmet Mercangöz |
|---|---|
| Primary category | eess.SY |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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