LLMs as Cognitive Partners in Shipping 4.0: An Extend AI Approach to Maritime Predictive Maintenance Training

Abstract

This paper explores the integration of Generative AI (Gen AI) and Explainable AI (XAI) into Predictive Maintenance (PdM) within the Shipping 4.0 framework for educational use. The authors propose a conceptual framework called IPMDP (Intelligent Predictive Maintenance Decision Process for Education), which involves an architectural approach (System-of-System, SoS) for integrating LLMs (Gen AI approach) into the predictive maintenance of marine engines and systems, in order to support decision-making in the maintenance of systems at sea. The central idea of the conceptual framework lies in the utilization of a decision-making model using Gen AI techniques through the ExtendAI framework, which functions as a “cognitive partner” to a ship’s engine room crew. The ultimate goal is the educational use of such a framework in the training of Merchant Marine engineers and other ship engine room personnel. Essentially, this study is a conceptual framework paper, integrating existing models and literature to propose a novel application domain, rather than presenting original empirical findings.  This cognitive maintenance approach improves operational safety and ensures human-centric, accountable decision-making, which is crucial for regulatory compliance and effective adoption in the maritime industry.