发表机构
University of Thessaly（色萨利大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对海事系统动态环境中的设备故障，提出一种集成数据采集、预测、检测、风险评估与可解释AI的闭环架构，支持自主或人在回路干预，并综述现状与挑战。
AI 中文摘要
海事系统在高度动态的环境中运行，意外的设备故障可能危及安全、可靠性和运营效率。人工智能（AI）、机器学习、数字孪生和预测性维护的最新进展使得主动的故障预测和预防成为可能。然而，在安全关键的海事应用中，确保可信和可解释的决策仍然是一个重大挑战。本章回顾了海事系统中可解释故障预测与预防所需的关键AI技术，并提出了一种能够支持自主或人在回路的纠正操作的概念架构。该架构将数据采集、时间序列预测、异常检测、风险评估、决策制定和可解释AI整合到一个闭环框架中。参照架构组件，对相关海事研究进行了回顾和讨论，概述了它们的方法、优点和局限性。此外，它强调了当前的挑战，包括不确定性和鲁棒性、模型泛化、可解释性、海事数据集的有限可用性以及实际部署，并指出了面向可信AI辅助海事决策的未来研究方向。
英文摘要
Maritime systems operate in highly dynamic environments where unexpected equipment failures can compromise safety, reliability, and operational efficiency. Recent advances in artificial intelligence (AI), machine learning, digital twins, and predictive maintenance enable proactive failure prediction and prevention. However, ensuring trustworthy and explainable decision-making remains a major challenge in safety-critical maritime applications. This chapter reviews key AI technologies required for explainable failure prediction and prevention in maritime systems and presents a conceptual architecture capable of supporting autonomous or human-in-the-loop corrective actions. This architecture integrates data acquisition, time-series forecasting, anomaly detection, risk assessment, decision-making, and explainable AI into a closed-loop framework. With reference to the architectural components, a review and discussion of relevant maritime studies is performed, outlining their methods, advantages, and limitations. Furthermore, it highlights current challenges, including uncertainty and robustness, model generalization, explainability, limited availability of maritime datasets, and operational deployment, and identifies future research directions toward trustworthy AI-assisted maritime decision-making.
