Physics-informed dynamic Bayesian networks for time-dependent reliability prediction of subsea wellhead sealing system with multi-states

IF 8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence Pub Date : 2025-09-29 DOI:10.1016/j.engappai.2025.112492

Shengnan Wu , Han Gong , Long Yu , Aibo Zhang , Laibin Zhang , Yiliu Liu

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引用次数: 0

Abstract

The Subsea Wellhead Sealing System (SWSS) is crucial for the safety of deepwater operating, yet its reliability assessment faces challenges from harsh environments and multi-factor interactions. This study developed a data-driven, physics-informed reliability assessment method combining Finite Element Analysis (FEA) and Dynamic Bayesian Networks (DBN). An FEA model is established based on metal sealing theory, and a data-driven reliability model is subsequently constructed through sampling analysis, with a numerical-to-state conversion method bridging FEA and DBN. The FEA-DBN approach offers two key advantages: eliminating expert scoring subjectivity through physics-based modeling and effectively capturing multi-factor interactions and time-dependent behaviors. Results show this method can precisely quantify the evolution of SWSS reliability throughout its service lifecycle, with the probability of failure increasing from 0.64 % to 3.38 % over a 30-year service life. Case studies demonstrate its effectiveness for deep-sea equipment assessment, particularly in operating environments where real-time monitoring proves challenging, thereby demonstrating significant engineering application value.

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基于物理信息的多状态水下井口密封系统时变可靠性预测动态贝叶斯网络

水下井口密封系统（SWSS）对于深水作业的安全至关重要，但其可靠性评估面临着恶劣环境和多因素相互作用的挑战。本研究开发了一种结合有限元分析（FEA）和动态贝叶斯网络（DBN）的数据驱动、物理信息的可靠性评估方法。基于金属密封理论建立了有限元模型，并通过采样分析建立了数据驱动的可靠性模型，采用数值-状态转换方法将有限元分析与DBN连接起来。FEA-DBN方法提供了两个关键优势：通过基于物理的建模消除专家评分的主观性，并有效捕获多因素相互作用和时间依赖行为。结果表明，该方法可以精确量化SWSS在整个使用寿命周期内的可靠性演变，在30年的使用寿命内，故障概率从0.64%增加到3.38%。案例研究证明了其在深海设备评估中的有效性，特别是在实时监测具有挑战性的操作环境中，从而展示了重要的工程应用价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Engineering Applications of Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

9.60

自引率

10.00%

发文量

505

审稿时长

68 days

期刊介绍： Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.