Reliability Engineering & System Safety最新文献

Global nickel scrap supply network vulnerability: Endogenous structural exposure and external shocks propagation 全球镍废料供应网络脆弱性：内生结构性暴露和外部冲击传播

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-31 DOI: 10.1016/j.ress.2026.112339

Xiaohong Chen , Daipeng Ma , Jian Guan

Against the backdrop of escalating geopolitical tensions, the global scrap nickel supply network (GSNSN) faces mounting challenges to its systemic reliability. This paper constructs an analytical framework integrating endogenous structural exposure with cascading failure simulations to assess the structural vulnerability mechanisms of the GSNSN. The results indicate that, from the perspective of endogenous structural exposure, the system exhibits significant characteristics of non-linear abrupt transitions, revealing the structural criticality of the network’s transition from a steady state to a collapse. Regarding external shocks, national import/export bans or disruptions in cooperation generally manifest into four risk propagation modes: long-range & large-scale, long-range & small-scale, short-range & large-scale, and short-range & small-scale. Specifically, high-coupling strategic corridors or nodes constitute the core of vulnerability due to rigid supply-demand dependencies (e.g., GBR→USA, DEU↔SWE, CHN, and USA), whereas nodes with high risk tolerance function as physical firewalls through a threshold dissipation mechanism. The findings emphasize that the governance paradigm for resource supply chains must shift from flow monitoring to topological optimization, suggesting that constructing strategic redundancy is critical for enhancing the resilience of the global supply network.

在地缘政治紧张局势不断升级的背景下，全球废镍供应网络（GSNSN）的系统可靠性面临日益严峻的挑战。本文构建了内源性结构暴露与级联破坏模拟相结合的分析框架，以评估GSNSN的结构脆弱性机制。结果表明，从内生结构暴露的角度看，系统表现出显著的非线性突变特征，揭示了网络从稳态向崩溃过渡的结构临界性。对于外部冲击，国家进出口禁令或合作中断通常表现为四种风险传播模式：远程大规模、远程小规模、短程大规模和短程小规模。具体来说，由于刚性的供需依赖关系（例如，GBR→USA, DEU↔SWE， CHN和USA），高耦合战略走廊或节点构成了脆弱性的核心，而具有高风险容忍度的节点通过阈值消散机制充当物理防火墙。研究结果强调，资源供应链的治理范式必须从流量监测转向拓扑优化，这表明构建战略冗余对于增强全球供应网络的弹性至关重要。

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引用次数: 0

Network recovery for UAV-assisted IoTs after cascading failures with heterogeneous graph neural networks 基于异构图神经网络的无人机辅助物联网级联故障网络恢复

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-30 DOI: 10.1016/j.ress.2026.112320

Xiaodian Zhuang , Xiuwen Fu , Liudong Xing , Rui Peng

With the growing adoption of unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT), its resilience against cascading failures has garnered significant attention. Cascading failures can severely compromise the topological integrity of such networks, making efficient recovery a significant challenge. To address this challenge, a Network Recovery scheme with Heterogeneous Graph neural network (NRHG) is proposed. The proposed scheme employs a Heterogeneous Graph Neural Network (HGNN), which includes graph perception layers processing local observations from individual UAVs, and graph communication layers enabling information exchange among UAVs. A multi-agent reinforcement learning (MARL) framework is further employed to enable collaborative action decisions for UAVs. Experimental results demonstrate that the proposed NRHG scheme can efficiently schedule surviving UAVs to cover the network blind spots caused by cascading failures. Compared to other schemes, the proposed scheme shows superior performance in both network coverage recovery and system throughput restoration.

随着无人机（UAV）辅助物联网（IoT）的日益普及，其抗级联故障的弹性已经引起了人们的广泛关注。级联故障会严重损害此类网络的拓扑完整性，使高效恢复成为一项重大挑战。针对这一挑战，提出了一种基于异构图神经网络（NRHG）的网络恢复方案。该方案采用异构图神经网络（HGNN），其中包括处理单个无人机局部观测的图感知层和实现无人机间信息交换的图通信层。进一步采用多智能体强化学习（MARL）框架实现无人机协同行动决策。实验结果表明，所提出的NRHG方案能够有效地调度幸存的无人机，覆盖由级联故障引起的网络盲点。与其他方案相比，该方案在网络覆盖恢复和系统吞吐量恢复方面都具有较好的性能。

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引用次数: 0

Reliability-driven adaptive multi-level pre-optimization control method for reusable launch vehicles under strong stochastic wind disturbances 强随机风扰动下可重复使用运载火箭可靠性驱动自适应多级预优化控制方法

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-30 DOI: 10.1016/j.ress.2026.112327

Dawen Huang

Strong stochastic wind disturbances pose a major threat to the landing reliability of reusable launch vehicles, potentially leading to attitude instability, trajectory deviation, and even structural damage. Traditional control methods, which heavily rely on precise dynamic models and online state observation or prediction, often struggle to ensure reliability under such conditions. To address this challenge, this work proposes a reliability-driven landing pre-optimization method that incorporates regional stochastic wind characteristics, thereby eliminating the need for online observation or prediction. The landing dynamics and stochastic wind field models are established to quantify the destructive impact of winds on landing reliability. An adaptive multi-level control strategy is then introduced, which hierarchically deploys simplified control laws based on real-time altitude and velocity. This design effectively compensates for time-varying wind disturbances without depending on online observers or pre-planned trajectories. Furthermore, a reliability-driven offline optimization framework is developed to tune the control parameters and landing initiation conditions. These key parameters are optimized offline through large-scale Monte Carlo simulations across diverse wind scenarios, thus avoiding the computational burden of online optimization. Finally, the optimal parameters and conditions are pre-optimized to adapt to regional stochastic winds. Results demonstrate that the proposed method achieves a landing reliability of >99.5% and reduces the maximum landing deviation by 99.52%. In comparative studies, the offline pre-optimization method shows superior performance to typical online Model Predictive Control, improving reliability by 14.1%. The proposed strategy offers a robust and practical solution for achieving high-reliability landings under strong stochastic winds.

强随机风扰动对可重复使用运载火箭的着陆可靠性构成重大威胁，可能导致姿态不稳定、轨迹偏离甚至结构损坏。传统的控制方法严重依赖于精确的动态模型和在线状态观测或预测，往往难以保证这种情况下的可靠性。为了应对这一挑战，本研究提出了一种可靠性驱动的着陆预优化方法，该方法结合了区域随机风特征，从而消除了在线观测或预测的需要。为了量化风对着陆可靠性的破坏性影响，建立了着陆动力学和随机风场模型。然后引入了一种自适应多级控制策略，该策略基于实时高度和速度分层部署简化的控制律。这种设计有效地补偿了时变的风干扰，而不依赖于在线观测者或预先规划的轨迹。在此基础上，建立了可靠性驱动的离线优化框架，对控制参数和起降条件进行了优化。这些关键参数通过大规模蒙特卡罗模拟在不同的风场景下进行离线优化，从而避免了在线优化的计算负担。最后，对最优参数和条件进行了预优化，以适应区域随机风。结果表明，该方法的着陆可靠性为99.5%，最大着陆偏差降低了99.52%。在对比研究中，离线预优化方法的性能优于典型的在线模型预测控制，可靠性提高14.1%。所提出的策略为在强随机风条件下实现高可靠性着陆提供了一种鲁棒性和实用性的解决方案。

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引用次数: 0

Beyond waterlogging: Evaluating the impact of extreme rainfall on the road network 超越内涝：评估极端降雨对道路网络的影响

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-29 DOI: 10.1016/j.ress.2026.112308

Jie Liu , Zizhen Xu , Li Wan , Kristen MacAskill

Existing research of extreme rainfall impact on transport networks primarily examines the effect of waterlogging. Although the other two main factors—reduced visibility and traffic-signal power outages—have been shown to significantly affect road operation, their contributions at the network scale remain underexplored. Taking a macroscopic approach, this study gauges the impacts of these three factors on the road network connectivity and efficiency during extreme rainfall through a case study of 26 Local Government Areas in and around Greater London. The result shows that focusing solely on waterlogging while disregarding reduced visibility and traffic signal power failures overestimates road capacities by 15–30% and underestimates network efficiency impacts by 1–23% under different rainfall scenarios. Particularly, the largest impact underestimation is observed for 1-in-30-year rainfall risk, where waterlogging is less dominant, while poor visibility considerably contributes to the impacts. The analysis also suggests that signal power failures during rainfall have limited, localised effects at the network level.

现有的极端降雨对交通网络影响的研究主要考察了内涝的影响。尽管其他两个主要因素——能见度降低和交通信号停电——已被证明会显著影响道路运行，但它们在网络规模上的作用仍未得到充分探讨。本研究采用宏观方法，通过对大伦敦及其周边26个地方政府区域的案例研究，衡量了这三个因素对极端降雨期间道路网络连通性和效率的影响。结果表明，在不同降雨情景下，仅关注内涝而忽视能见度降低和交通信号故障对道路通行能力的高估幅度为15-30%，对路网效率影响的低估幅度为1-23%。特别是，对30年一遇的降雨风险的影响低估最大，其中内涝不太占主导地位，而能见度低在很大程度上助长了影响。该分析还表明，降雨期间的信号电源故障在网络层面上具有有限的、局部的影响。

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引用次数: 0

Analysis of urban hydrogen-blended natural gas pipeline leak failure and accident evolution based on the combination of causal inference and probabilistic machine learning 基于因果推理和概率机器学习相结合的城市混氢天然气管道泄漏故障及事故演变分析

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-29 DOI: 10.1016/j.ress.2026.112323

Wuyin Lin , Songming Yu , Xinran Yu , Yuxing Li , Cuiwei Liu

Integrating hydrogen into urban gas pipeline networks is a pivotal technology for energy transition yet poses critical safety threats, thus necessitating comprehensive risk assessment of hydrogen-blended natural gas pipelines. This study performs full quantitative risk assessment of leakage failure and accident evolution by proposing a novel framework that integrates causal inference (Bow-Tie analysis) with probabilistic machine learning (Bayesian networks), enabling systematic failure factor identification and dynamic accident progression simulation. Key findings indicate human factors and pipeline material degradation as primary triggers. The studied pipeline exhibits a low baseline failure probability, with dispersion emerging as the most likely consequence of leakage. Higher hydrogen blending ratios significantly elevate jet fire risk due to hydrogen’s low ignition energy, while hydrogen’s inherent buoyancy and high diffusivity notably mitigate the likelihood of flash fire and vapor cloud explosion. The case study verifies the model’s practicability, and macro-micro analyses provide holistic insights, offering a reliable method to guide pipeline safety and reliability improvement amid energy transition.

氢气融入城市燃气管网是能源转型的关键技术，但也存在严重的安全威胁，因此有必要对氢气混合天然气管道进行综合风险评估。本研究通过提出一种将因果推理（Bow-Tie分析）与概率机器学习（贝叶斯网络）相结合的新框架，对泄漏故障和事故演变进行了全面的定量风险评估，从而实现了系统的故障因素识别和动态事故进展模拟。主要研究结果表明，人为因素和管道材料降解是主要诱因。所研究的管道显示出较低的基线失效概率，泄漏最可能的结果是分散。由于氢的点火能较低，较高的氢混合比例显著提高了喷射火灾的风险，而氢固有的浮力和高扩散系数显著降低了闪火和蒸汽云爆炸的可能性。通过实例分析，验证了模型的实用性，宏观微观分析提供了整体洞见，为指导能源转型背景下管道安全可靠性提升提供了可靠的方法。

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引用次数: 0

Multi-dimensional sequence embedding and improved Informer for prediction of industrial alarm events 面向工业报警事件预测的多维序列嵌入和改进的Informer

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-29 DOI: 10.1016/j.ress.2026.112317

Wenbin Jiang , Wenkai Hu , Yupeng Li , Weihua Cao

As an effective alarm monitoring strategy, alarm event prediction helps mitigate the impact of alarm floods and the risk of industrial accidents by providing early warnings of potential future alarms, thereby allowing operators more time to take corrective action. However, in continuous industrial processes, varying operating conditions and abnormal states cause real-time fluctuations in alarm rates, posing challenges for existing methods to achieve satisfactory prediction performance. In view of such issues, this paper proposes a new alarm event prediction method adapting to variable alarm rates over long-term consecutive alarm monitoring periods using multi-dimensional sequence embedding and improved Informer. The contributions are threefold: 1) An adaptive alarm sequence segmentation strategy is designed to generate input alarm sequences adapting to alarm rates; 2) a multi-dimensional sequence embedding method based on both the alarm tags and time intervals is proposed to convert the textual alarm messages into numerical vectors; and 3) an Informer based alarm event prediction model is developed for precise and early alarm event prediction under alarm flood and non-flood periods. A case study based on the Vinyl Acetate Monomer public model is given to prove the effectiveness of the proposed method.

作为一种有效的报警监测策略，报警事件预测通过提供潜在未来报警的早期预警，有助于减轻报警洪水的影响和工业事故的风险，从而使运营商有更多的时间采取纠正措施。然而，在连续的工业过程中，不同的运行条件和异常状态会导致报警率的实时波动，这对现有方法实现令人满意的预测性能提出了挑战。针对这些问题，本文提出了一种基于多维序列嵌入和改进的Informer的适应长期连续报警监测周期内变报警率的报警事件预测新方法。主要贡献有三：1)设计了一种自适应报警序列分割策略，生成适应报警率的输入报警序列；2)提出了一种基于报警标签和时间间隔的多维序列嵌入方法，将文本报警信息转化为数值向量；3)建立了基于Informer的预警事件预测模型，实现了预警洪涝期和非洪涝期预警事件的准确预警。以醋酸乙烯单体公共模型为例，验证了该方法的有效性。

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引用次数: 0

Transformer-augmented deep Q-network-based risk-informed maintenance policy for partially observable systems under combined degradation and random shock effects 退化和随机冲击联合作用下部分可观测系统的基于变压器增强深度q网络的风险知情维护策略

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-28 DOI: 10.1016/j.ress.2026.112301

Chunhui Guo, Zhenglin Liang

Many real-world systems experience both natural degradation and random shocks, with degradation often assessed using only partial information. When both factors are considered, the underlying degradation process may carry a high risk of transitioning rapidly to a more severe state, making the interpretation of partial observations particularly challenging. To address this challenge, we formulate partially observable systems under combined natural degradation and random shock effects as a partially observable continuous-time Markov model. Based on this model, we introduce a risk-informed inspection and maintenance policy that schedules inspections according to a predefined risk threshold, aiming to reduce costs. We demonstrate that the optimal maintenance approach follows a control-limit policy, applied at decision epochs determined by the evolving risk profile. Leveraging this structural insight, we design a tailored Transformer-augmented Deep Q-Network algorithm to effectively optimize the inspection and maintenance policy under partial observation, which is regarded as a novel and online algorithm for the Partially Observable Markov Decision Process with a multi-dimensional continuous state space. The proposed approach is validated through a case study involving lithium-ion battery maintenance. The results reveal that our approach achieves an average reduction of 57.4% in inspection costs compared to traditional periodic inspection schemes.

许多现实世界的系统经历了自然退化和随机冲击，退化通常仅使用部分信息进行评估。当考虑到这两个因素时，潜在的退化过程可能具有迅速过渡到更严重状态的高风险，这使得部分观测结果的解释特别具有挑战性。为了解决这一挑战，我们将自然退化和随机冲击联合作用下的部分可观察系统表述为部分可观察的连续时间马尔可夫模型。基于该模型，我们引入了一种风险知情的检查和维护策略，该策略根据预定义的风险阈值安排检查，旨在降低成本。我们证明了最优维护方法遵循控制-限制策略，应用于由不断变化的风险概况决定的决策时期。利用这种结构洞察力，我们设计了一种定制的变压器增强深度Q-Network算法，以有效地优化部分观测下的检查和维护策略，这被认为是一种新颖的具有多维连续状态空间的部分可观察马尔可夫决策过程的在线算法。通过一个涉及锂离子电池维护的案例研究验证了所提出的方法。结果表明，与传统的定期检测方案相比，我们的方法平均降低了57.4%的检测成本。

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引用次数: 0

Distributionally robust fairness-based last-mile relief network optimization with casualty uncertainty 考虑人员伤亡不确定性的分布式鲁棒公平性最后一英里救助网络优化

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-28 DOI: 10.1016/j.ress.2026.112305

Guoqing Yang, Hongye Yuan, Wenshuai Yang, Ruru Jia

The suddenness and the casualties’ uncertainty of natural disasters urgently require a fair and robust network design for the medical supply distribution and the injured evacuation to reduce their post-disaster impact. This study establishes a distributionally robust chance-constrained model for medical supplies allocation in last-mile relief networks, with the objective of minimizing the worst-case Conditional Value-at-Risk (CVaR) of supply shortages. The distribution of severely injured casualties is characterized via a scenario-wise ambiguity set, thereby the proposed model is reformulated as a mixed-integer linear programming problem for tractability. Numerical experiment based on Wenchuan earthquake derives several important findings. First, total supplies and raw materials exhibit analogous effects—increasing either reduces shortage levels initially, but further reductions are constrained by the other factor; Second, in response to high risks, the tendency is to build additional medical stations rather than expanding the scale of existing ones to disperse risk. Conversely, when risk is low, scaling up existing medical stations is preferred over establishing temporary facilities; Finally, under out-of-sample data fluctuations, the CVaR model demonstrates stronger robustness than the sample average approximation model, with consistently smaller standard deviations and superior stability.

自然灾害的突发性和伤亡的不确定性，迫切需要一个公平、稳健的医疗物资分配和伤员后送网络设计，以减少其灾后影响。本研究以最小化供应短缺的最坏情况条件风险价值（CVaR）为目标，建立了最后一英里救援网络中医疗用品分配的分布鲁棒机会约束模型。通过场景模糊集表征重伤员的分布，从而将模型重新表述为可追溯性的混合整数线性规划问题。基于汶川地震的数值实验得出了几个重要的发现。首先，总供应量和原材料表现出类似的效应——要么在最初减少短缺水平，但进一步的减少受到其他因素的限制；第二，针对高风险，倾向于增加医疗站，而不是扩大现有医疗站的规模，以分散风险。相反，当风险较低时，扩大现有医疗站比建立临时设施更可取；最后，在样本外数据波动情况下，CVaR模型比样本平均近似模型具有更强的稳健性，具有一贯较小的标准差和更优越的稳定性。

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引用次数: 0

Resilience assessment and enhancement of urban transportation interdependent network under cascading failure 级联故障下城市交通相互依赖网络的恢复力评价与增强

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-28 DOI: 10.1016/j.ress.2026.112302

Meng Li, Yu-Rong Song, Bo Song, Guo-Ping Jiang

Urban transportation systems are essential for sustaining urban growth and ensuring efficient resource allocation. Existing studies primarily focus on evaluating network resilience after system disturbances, with insufficient attention paid to the response mechanisms during disturbances and the enhancement of resilience afterward. Therefore, we propose a cascading failure model that considers passenger transfer impedance, and design a recovery priority strategy for failed nodes to maximize the resilience of the urban transportation interdependent network (UTIN). Specifically, based on traffic sensing data, we construct a station-centric UTIN to assess structural resilience under various disruption scenarios and different transfer distances. By combining impedance function and flow redistribution, passenger behavior and node load update are considered. Additionally, the recovery priority strategy for failed nodes is discussed. The results indicate: 1) UTINs with longer transfer distances exhibit stronger resistance to risks. When considering impedance costs, the optimal transfer distance is 800 m. 2) During cascading failure propagation, optimizing flow distribution effectively lowers the critical capacity threshold required for system stability, thereby enhancing network resilience. 3) During the recovery phase, different recovery strategies exhibit significant differences in their effectiveness in restoring system resilience. The research findings provide valuable references for disaster prevention, emergency response, and post-disaster recovery in urban transportation systems.

城市交通系统对于维持城市增长和确保有效的资源配置至关重要。现有的研究主要集中在系统扰动后网络弹性的评估上，对扰动时的响应机制和扰动后弹性的增强关注不足。因此，我们提出了考虑乘客转移阻抗的级联故障模型，并设计了故障节点的恢复优先策略，以最大限度地提高城市交通相互依赖网络（UTIN）的弹性。具体而言，基于交通感知数据，我们构建了一个以站点为中心的utn来评估各种中断场景和不同传输距离下的结构弹性。结合阻抗函数和流量再分配，考虑了乘客行为和节点负荷更新。此外，还讨论了故障节点的恢复优先级策略。结果表明：1)迁移距离越远的UTINs对风险的抵抗力越强。考虑阻抗成本时，最优传输距离为800 m。2)在级联故障传播过程中，优化流量分布可以有效降低系统稳定所需的临界容量阈值，从而增强网络的弹性。3)在恢复阶段，不同的恢复策略对恢复系统弹性的效果存在显著差异。研究结果为城市交通系统的灾害预防、应急响应和灾后恢复提供了有价值的参考。

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引用次数: 0

Future directions for data-driven approaches in pipeline integrity management: Risk assessment, in-line inspection, and machine learning 管道完整性管理中数据驱动方法的未来发展方向：风险评估、在线检查和机器学习

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-27 DOI: 10.1016/j.ress.2026.112300

Tim Bastek , Jens Denecke , Jürgen Schmidt

Gas pipeline failure continues to be a serious hazard for people in the vicinity of gas pipelines, particularly given the increase in urban development and aging infrastructure. This study critically reviews the current state and potential of data-driven approaches in pipeline integrity management systems (PIMS) for most critical threats. In addition to a purely theoretical discussion, three illustrative case studies are used to highlight the main limitations in the following areas: a) third-party damage assessment, b) the quality of in-line-Inspection (ILI) data and c) machine learning-based external corrosion evaluation. A quantitative risk analysis was performed to analyze shortcomings in context of current prevention practices. Research gaps lie in the evaluation of probability of failure insufficiently dependent on the gas pipeline location but in practice on pipeline design. A new GIS-based, probabilistic approach was proposed to assess TPD using available environmental data. Secondly, published ILI data was analyzed, which reveals a large amount of corrosion detected over pipeline route, but low replicability from one ILI run to another - limiting usage in PIMS and data driven modelling. Thirdly, a hybrid support vector regression model was trained to predict external corrosion, but its performance proved unstable: prediction accuracy dropped by 27% during cross-validation, highlighting the practical risks of model overfitting. This study highlights the need for more robust, context-sensitive models and outlines potential advancements to improve pipeline safety and system reliability using data-driven strategies.

天然气管道故障仍然是天然气管道附近居民的严重危害，特别是考虑到城市发展的增加和基础设施的老化。本研究批判性地回顾了管道完整性管理系统（PIMS）中数据驱动方法的现状和潜力，以应对大多数关键威胁。除了纯粹的理论讨论之外，本文还使用了三个说白了的案例研究来强调以下领域的主要局限性：a)第三方损伤评估，b)在线检测（ILI）数据的质量，以及c)基于机器学习的外部腐蚀评估。进行了定量风险分析，以分析当前预防措施的不足之处。研究的空白在于失效概率的评估不完全依赖于输气管道的位置，而实际依赖于管道的设计。提出了一种新的基于gis的概率方法，利用现有的环境数据来评估TPD。其次，对公布的ILI数据进行了分析，发现在管道路线上检测到大量腐蚀，但从一次ILI到另一次ILI的可复制性较低，这限制了PIMS和数据驱动建模的使用。第三，训练混合支持向量回归模型预测外部腐蚀，但其性能不稳定：在交叉验证过程中预测精度下降了27%，突出了模型过拟合的实际风险。该研究强调了对更强大、环境敏感的模型的需求，并概述了使用数据驱动策略提高管道安全性和系统可靠性的潜在进展。

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引用次数: 0