防止船舶碰撞和搁浅的系统驱动智能决策支持方法:现状、可能的解决方案和挑战

IF 9.4 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL
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引用次数: 0

摘要

尽管科学技术在不断进步,但船舶碰撞和搁浅仍是最常见的海上事故类型。自主航运、数字技术和人工智能(AI)的兴起推动了事故预防和缓解方法的最新发展。本文详尽回顾了过去二十年来面临风险的船队的特点,强调了预防碰撞和搁浅的社会影响。它还从船舶的角度深入探讨了决策支持系统的关键组成部分,并对预防船舶碰撞和搁浅的系统驱动决策支持方法的基础和应用进行了系统的文献综述。研究涵盖了从 2002 年到 2023 年的风险分析、损害评估和船舶运动预测方法。研究结论表明,现代船舶科学方法在船舶设计和海上作业中的价值与日俱增。新兴的多物理场系统和人工智能支持的预测分析方法显示了未来集成到智能决策支持系统中的潜力。战略性研究挑战包括:(1)低估实际操作条件对船舶安全的影响;(2)静态风险分析和有限数值方法的固有局限性;(3)对损害范围进行快速、概率评估的需求。这些需求和趋势表明,在数字化和人工智能技术的支持下,利用大数据分析和快速预测方法是最可行的发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Systems driven intelligent decision support methods for ship collision and grounding prevention: Present status, possible solutions, and challenges

Despite advancements in science and technology, ship collisions and groundings remain the most prevalent types of maritime accidents. Recent developments in accident prevention and mitigation methods have been bolstered by the rise of autonomous shipping, digital technologies, and Artificial Intelligence (AI). This paper provides an exhaustive review of the characteristics of fleets at risk over the past two decades, emphasizing the societal impacts of preventing collisions and groundings. It also delves into the key components of decision support systems from a ship's perspective and undertakes a systematic literature review on the foundations and applications of systems-driven decision support methods for ship collision and grounding prevention. The study covers risk analysis, damage evaluation, and ship motion prediction methods from 2002 to 2023. The conclusions indicate that modern ship science methods are increasingly valuable in ship design and maritime operations. Emerging multi-physics systems and AI-enabled predictive analytics show potential for future integration into intelligent decision support systems. The strategic research challenges include (1) underestimating the impacts of real operational conditions on ship safety, (2) the inherent limitations of static risk analysis and finite numerical methods, and (3) the need for rapid, probabilistic assessments of damage extents. The demands and trends suggest that leveraging big data analytics and rapid prediction methods, underpinned by digitalization and AI technologies, represents the most feasible way forward.

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来源期刊
Reliability Engineering & System Safety
Reliability Engineering & System Safety 管理科学-工程:工业
CiteScore
15.20
自引率
39.50%
发文量
621
审稿时长
67 days
期刊介绍: Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.
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