arXiv:2606.31614v1 Announce Type: cross Abstract: 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, symp
The convergence of mature knowledge graph technology and increasingly capable, constrained large language models makes automation of complex engineering specifications feasible now.
Automating cause-effect specification addresses a critical bottleneck in industrial automation and safety, reducing human error and accelerating deployment in complex systems.
The manual, document-driven creation of critical industrial specifications can now be significantly automated, shifting from bespoke human effort to AI-assisted generation.
- · Industrial automation sector
- · Process control companies
- · Large Language Model developers
- · Knowledge Graph developers
- · Consultants specializing in manual specification drafting
- · Legacy document-based engineering processes
This framework accelerates the design and implementation of safety-critical industrial systems by automating C&E logic generation.
It will likely lead to more consistent and reliable industrial operations by reducing human-induced inconsistencies in specifications.
This could enable greater complexity and scale in automated industrial systems, as the bottleneck of manual specification is alleviated.
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Read at arXiv cs.AI