Approaches to automatic discovery and modeling of Industrial Assets for IT/OT Integration ^*

Data integration in brownfield industrial environments is an essential step to realize the benefits of Industry 4.0 and information technology (IT)/operational technology (OT) convergence, but requires significant effort to connect, discover, identify, organize, and model the runtime data available on the relevant assets. Two methods are implemented to automatically construct OPC-UA (Open Platform Communications Unified Automation) information models from discovered assets. The first is based on large language models (LLMs) and knowledge graph-based retrieval augmented generation (GraphRAG), while the second employs text clustering to map data points (e.g., tags) to the appropriate OPC UA type model. The methods are compared by executing generation tasks for target models of differing complexity and assessing syntactical and semantic accuracy. The results suggest that, while GraphRAG based approaches are capable of generating accurate and compliant results for simple target semantic models, for more complex tasks, they struggle to match the performance of more traditional domain-specific algorithms. By providing a holistic system and methodology for asset discovery and modeling, this research aims to significantly minimize engineering efforts and enable seamless IT/OT integration in industrial environments.