arXiv：cs.AI· Dionisis Kalogeropoulos, Georgia Sovatzidi, Panagiotis G. Kalozoumis, Dimitris K. Iakovidis·· 6 小时前AI 评分23
海事系统中的可解释故障预测与预防
Explainable Failure Prediction and Prevention in Maritime
AI 导读
一篇综述提出面向海事系统的可解释故障预测与预防概念架构，将数据采集、时间序列预测、异常检测、风险评估、决策与可解释 AI 整合为闭环框架，以支持自主或人在环的纠正措施。文章结合该架构梳理相关海事研究的方法、优势与局限，并指出不确定性、鲁棒性、模型泛化、可解释性、海事数据集稀缺和实际部署等挑战及未来方向。
正文
Abstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.08363 [cs.AI] |
| (or arXiv:2610.08363v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08363 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Dimitris Iakovidis [view email]
[v1]
Tue, 6 Oct 2026 13:51:58 UTC (969 KB)
来源：arXiv：cs.AI · arxiv.org