API-Based Integration Framework for Dual-LLM Prescriptive Maintenance Report Generation in PHM-Enabled Digital Twin Applications
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Abstract
Digital Twins (DTs) have emerged as key enablers of Prognostics and Health Management (PHM) for predicting asset failures and optimizing maintenance strategies. However, translating predictive insights into actionable prescriptive maintenance plans remains a significant implementation challenge. This paper proposes an API-based framework that extends PHM-enabled DTs by incorporating a prescriptive maintenance layer aligned with enterprise operational constraints, including workforce scheduling, inventory availability, cost considerations, and compliance requirements. The framework integrates a generator model to produce maintenance recommendations and a checker model to evaluate report quality against operational criteria using a sequential model loading approach. Prescriptive maintenance reports using enterprise data as context are generated for a hydraulic system undergoing MCD scenarios. The framework proposed in this paper provides a low-cost implementation for integrating LLMs for prescriptive maintenance reporting for DT applications. This study contributes to LLM implementation use cases for DT applications for fleet asset management.
How to Cite
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Application Programming Interface, Digital Twin, Generative Adversarial Network, Large Language Models, Resource Availability Data, Prognostic and Health Management
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