Large Language Model-Augmented Semantic Digital Twins for Real-Time Fault Diagnosis and Closed-Loop Control

Abstract

Abstract. Modern manufacturing systems demand cognitive digital twins (CDTs) capable of not only mirroring physical processes but also interpreting context and reasoning about system behavior. Traditional digital twins (DTs), while effective in replication, lack these cognitive abilities—limiting their usefulness for trustworthy, transparent, and adaptive decision-making in smart manufacturing environments. This article presents a neuro-symbolic CDT framework that unifies ontology-based modeling with large language models (LLMs) through a retrieval-augmented generation (RAG) architecture to enable explainable fault diagnosis and decision support. The ontology captures domain knowledge on faults, corrective actions, and rule-based logic using the semantic web rule language (SWRL), forming a structured layer for cognitive reasoning. Simulation data from a virtual machine cell are semantically mapped to the ontology to construct a knowledge graph (KG) that contextualizes real-time operational data. Leveraging this graph, the RAG pipeline allows LLMs to retrieve structured insights and generate human-interpretable explanations as well as machine-actionable commands, effectively bridging neural and symbolic reasoning. Demonstrated use cases show that integrating rule-based reasoning with neural generation enhances fault detection, interpretability, and adaptive control, charting a path toward CDTs that are both transparent to users and operationally effective in real time.