Agentic LLMs for Scalable, Verifiable System Health Digital Twins

Abstract

System Health Management (SHM) digital twins have evolved from specialized engineering tools into enterprise-wide critical systems supporting diagnostics and lifecycle decision support, yet scaling the creation, validation, and maintenance of detailed causal models remains a bottleneck due to labor-intensive, expert-driven processes that do not scale with system complexity or lifecycle evolution. This paper presents an AI-driven framework addressing this challenge through a tightly integrated neuro-symbolic architecture that combines agentic large language models (LLMs) as constrained knowledge extraction agents with a rigorous symbolic reasoning core grounded in multi-functional causal modelling, enforcing structural, semantic, and logical constraints to transform extracted knowledge into verifiable, executable diagnostic models while shifting human expertise toward validation, governance, and continuous improvement. The framework implements an end-to-end “ingest–extract–structure–verify” pipeline converting artifacts (i.e., technical manuals, schematics, FMECA data) into formal causal models compatible with TEAMS and SysML-based representations, providing a single source of truth for downstream applications including fault detection and isolation, prognostics, sensor optimization, training scenario generation, and lifecycle-informed design. Demonstrated results show up to an 80% reduction in engineering effort and rapid model generation at previously impractical scales, with aerospace and space system deployments confirming accurate, scalable operational reasoning, while an enterprise operating model treats the digital twin as a governed, evolving asset integrated across design, operations, maintenance, and training, enabling continuous adaptation from field data and offering a practical path to trustworthy, adaptive digital twins that deliver sustained enterprise-scale value.

Keywords: Agentic LLMs, System Health Digital Twins, Model Based Systems Engineering, Systems Modelling

DOI: 10.54941/ahfe1007674