arXiv:2606.31635v1 Announce Type: cross Abstract: Fault recovery in process plants still relies heavily on plant operators, especially when faults fall outside predefined supervisory logic. Operators interpret alarms, procedures, P\&IDs, interlocks, and process trends, then decide how to move the plant to a safe operating mode without triggering a shutdown. This paper examines how Large Language Model (LLM) agents can support such recovery decisions. The proposed framework treats the LLM as a constrained supervisory planner. It uses plant-specific knowledge to propose recovery actions, and eve
The rapid advancement of Large Language Models (LLMs) and their integration into agentic frameworks is enabling new levels of automation in critical industrial processes.
This development indicates a significant step towards autonomous industrial operations, reducing human dependency in complex fault recovery scenarios and increasing plant reliability and safety.
Industrial fault recovery, traditionally reliant on human operators, can now be augmented or potentially led by knowledge-grounded LLM agents, shifting operational paradigms.
- · Industrial automation companies
- · Process plant operators
- · LLM developers
- · Industrial AI platforms
- · Legacy industrial control systems
- · Manual fault-diagnosis service providers
LLM agents begin to be integrated into supervisory control systems across various industries.
There is a significant increase in the adoption of AI-driven autonomous systems in hazardous or complex industrial environments, leading to fewer human interventions during critical events.
The development of highly robust and globally interconnected autonomous industrial control networks diminishes localized operational expertise, raising new cybersecurity and systemic risk concerns.
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Read at arXiv cs.AI