AI-Enhanced Operator Assistance for UNICOS Applications

TL;DR

This paper presents an AI-enhanced multi-agent system for CERN's UNICOS control system that reduces operator workload, improves diagnostics, and accelerates response times through widget decoding, root cause analysis, and data tracing.

Contribution

It introduces a modular, multi-modal AI assistant architecture that integrates with UNICOS, enabling automated diagnostics and documentation retrieval to support industrial control operations.

Findings

System can decode widgets and analyze root causes using live data.

Reduces manual effort for operators and maintainers.

Enhances situational awareness and speeds up anomaly responses.

Abstract

This project explores the development of an AI-enhanced operator assistant for UNICOS, CERN's UNified Industrial Control System. While powerful, UNICOS presents a number of challenges, including the cognitive burden of decoding widgets, manual effort required for root cause analysis, and difficulties maintainers face in tracing datapoint elements (DPEs) across a complex codebase. In situations where timely responses are critical, these challenges can increase cognitive load and slow down diagnostics. To address these issues, a multi-agent system was designed and implemented. The solution is supported by a modular architecture comprising a UNICOS-side extension written in CTRL code, a Python-based multi-agent system deployed on a virtual machine, and a vector database storing both operator documentation and widget animation code. Preliminary evaluations suggest that the system is capable of decoding widgets, performing root cause analysis by leveraging live device data and documentation, and tracing DPEs across a complex codebase. Together, these capabilities reduce the manual workload of operators and maintainers, enhance situational awareness in operations, and accelerate responses to alarms and anomalies. Beyond these immediate gains, this work highlights the potential of introducing multi-modal reasoning and retrieval augmented generation (RAG) into the domain of industrial control. Ultimately, this work represents more than a proof of concept: it provides a basis for advancing intelligent operator interfaces at CERN. By combining modular design, extensibility, and practical AI integration, this project not only alleviates current operator pain points but also points toward broader opportunities for assistive AI in accelerator operations.