Reliability Engineering & System Safety最新文献_第2页

A synergistic approach: multi-purpose K-nearest neighbor and active learning Kriging for efficient failure probability function estimation 一种协同方法：多用途k近邻与主动学习Kriging的有效失效概率函数估计

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.2026.112295

Huanhuan Hu , Pan Wang , Fukang Xin , Zheng Zhang , Haihe Li , Jiahua Zhang

The failure probability concerning specified design parameters, termed the failure probability function (FPF), is essential in reliability-based design. Conventional methods require high computational costs for complex systems due to repeated expensive simulations. Although single-loop methods with active learning Kriging (AK) have been proposed to reduce these costs, their efficiency remains limited by suboptimal sampling and inaccurate kernel density estimation (KDE). To address these challenges, this work introduces a novel multi-purpose K-nearest neighbor (KNN) framework integrated with an enhanced AK in an augmented space, termed the SL-AK-KNN method. The method leverages the adaptive capabilities of KNN in two key aspects: (1) as a spatial-information-guided learning function that improves both global and local efficiency of AK by exploring and exploiting sample density variations across different regions, and (2) as an adaptive nonparametric density estimator for approximating the conditional joint probability density function (PDF), thereby mitigating KDE’s edge region inaccuracies without relying on kernel functions and fixed bandwidth. It is intuitively well-suited for exploratory analysis of unknown density distributions. Numerical examples demonstrate that the proposed framework significantly reduces computational costs while enhancing FPF estimation accuracy, enabling robust reliability design for the engineering applications of the bracket structure and hydraulic pipeline system.

在基于可靠性的设计中，有关特定设计参数的失效概率被称为失效概率函数（FPF）。对于复杂的系统，传统方法由于需要进行多次昂贵的模拟，计算成本较高。虽然已经提出了带有主动学习Kriging （AK）的单回路方法来降低这些成本，但它们的效率仍然受到次优采样和不准确的核密度估计（KDE）的限制。为了应对这些挑战，本研究引入了一种新的多用途k -最近邻（KNN）框架，该框架集成了增强空间中的AK，称为SL-AK-KNN方法。该方法在两个关键方面利用了KNN的自适应能力：(1)作为一个空间信息引导的学习函数，通过探索和利用不同区域的样本密度变化来提高AK的全局和局部效率；(2)作为一个自适应非参数密度估计器，用于近似条件联合概率密度函数（PDF），从而在不依赖核函数和固定带宽的情况下减轻KDE的边缘区域不准确性。直观上，它非常适合于未知密度分布的探索性分析。数值算例表明，该框架显著降低了计算成本，提高了FPF估计精度，为支架结构和液压管路系统的工程应用提供了鲁棒可靠性设计。

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引用次数: 0

Multi-ship collision risk situation assessment based on finite interval cloud model 基于有限区间云模型的多船碰撞风险态势评估

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.2026.112290

Hongzhen Wang , Zhengjiang Liu , Xiang-Yu Zhou , Lianbo Li , Shanshan Fei , Xinjian Wang

The assessment of multi-ship collision risk situation holds important theoretical value and practical significance for enhancing waterborne vessel safety supervision and ensuring safe navigation. However, maritime multi-ship navigation risks are often influenced by the coupled influence of hydro-meteorological conditions and multi-ship navigation situations, exhibiting significant uncertainty and fuzziness. In order to address those gaps, this study aims to propose a collision risk assessment method for multi-ships. First, a dual-dimensional evaluation indicator system integrating hydro-meteorological factors and multi-ship characteristics was constructed, accompanied by six calculation methods for indicator values, providing an operational basis for accurate risk assessment. Subsequently, game theory was employed to integrate weighting results derived from the best-worst method and the extension correlation function method, so as to mitigate the one-sidedness of a single weighting approach. Finally, based on the designed indicator interval grades, a finite interval cloud generator was constructed to characterize the fuzziness and uncertainty of the indicators, thereby achieving a precise quantitative rating of multi-ship collision risk. Validation through four groups of multi-ship potential encounter scenarios in the Bohai Sea of China shows that the proposed method can accurately distinguish the risk levels of different scenarios. Moreover, the variance of the evaluation results is 1 to 4.17 times that of the traditional extension cloud model, indicating higher confidence and sensitivity. The method provides objective and precise technical support for navigation situation monitoring in multi-ship potential encounter scenarios.

多船碰撞风险态势评估对于加强水上船舶安全监管，保障水上船舶安全航行具有重要的理论价值和现实意义。然而，海上多船航行风险往往受到水文气象条件和多船航行情况的耦合影响，具有显著的不确定性和模糊性。为了解决这些不足，本研究旨在提出一种多船碰撞风险评估方法。首先，构建了综合水文气象因素和多船特征的多维评价指标体系，并给出了6种指标值计算方法，为准确进行风险评估提供了操作依据。随后，利用博弈论对最佳-最差法和可拓相关函数法的加权结果进行整合，以减轻单一加权方法的片面性。最后，在设计指标区间等级的基础上，构建有限区间云发生器来表征指标的模糊性和不确定性，从而实现多船碰撞风险的精确定量评级。渤海海域4组多船潜在相遇场景验证表明，该方法能够准确区分不同场景的风险等级。评价结果的方差是传统扩展云模型的1 ~ 4.17倍，具有较高的置信度和灵敏度。该方法为多船潜在相遇场景下的航行态势监测提供了客观、精确的技术支持。

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引用次数: 0

The role of vehicle kinematic volatilities in expressway crash prediction and analysis: A two-part framework from road features to crashes 车辆运动波动在高速公路碰撞预测与分析中的作用：从道路特征到碰撞的两部分框架

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-26 DOI: 10.1016/j.ress.2026.112297

Pengcheng Qin, Haoze Chen, Zhiming Fang, Jie He, Xintong Yan, Changjian Zhang

To identify high-risk road sections and understand their mechanisms, we develop a two-part framework intermediated by vehicle kinematic volatilities. First, we use Generalized Autoregressive Conditional Heteroscedasticity Models to model volatilities of tri-axial accelerations and angular velocities, and Standard Ordered Logit models to examine how alignment and infrastructure features influence these volatilities. Second, we build an ensemble Soft Voting Classifier that combines Gradient Boosting, eXtreme Gradient Boosting, and Multi-layer Perceptron to predict high- and low-risk sections using kinematic volatility levels. The classifier achieves an accuracy of 0.911 with balanced performance across risk levels. The framework clarifies the role of kinematic volatilities in the causal pathway from inherent risk features to crash occurrence. Feature importance analysis identifies lateral acceleration, yaw rate, longitudinal acceleration, and pitch rate volatilities as critical indicators of high-risk sections. Practically, the framework can be implemented with inertial sensors on instrumented vehicles to screen crash risk along the entire expressway and prioritize high-risk sections for targeted improvement according to feature importance. In vehicle-infrastructure cooperation environments, the framework supports real-time risk monitoring and safety management by transmitting kinematic volatilities and risk alert information between connected vehicles and the control center.

为了识别高风险路段并了解其机制，我们开发了一个由车辆运动波动作为中介的两部分框架。首先，我们使用广义自回归条件异方差模型来模拟三轴加速度和角速度的波动，并使用标准有序Logit模型来研究排列和基础设施特征如何影响这些波动。其次，我们构建了一个集成的软投票分类器，该分类器结合了梯度增强、极端梯度增强和多层感知器，利用运动波动水平预测高风险和低风险部分。该分类器的准确率为0.911，在各个风险水平上性能平衡。该框架阐明了运动波动在从固有风险特征到崩溃发生的因果路径中的作用。特征重要性分析将横向加速度、偏航速度、纵向加速度和俯仰速度波动性确定为高风险路段的关键指标。在实践中，该框架可以通过仪表车辆上的惯性传感器来实现，以筛选整个高速公路的碰撞风险，并根据特征的重要性对高风险路段进行优先排序，进行有针对性的改进。在车辆-基础设施合作环境中，该框架通过在联网车辆和控制中心之间传输运动波动和风险警报信息，支持实时风险监控和安全管理。

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引用次数: 0

Adaptive Wiener process modeling integrating physical causality and degradation states for high-reliability systems 集成物理因果关系和退化状态的高可靠性系统自适应Wiener过程建模

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-26 DOI: 10.1016/j.ress.2026.112291

Tingting Zhang , Zenggui Gao , Rongfei Chen , Lilan Liu

Performance degradation of high-reliability equipment often involves challenges, particularly the coupling of multiple components and the concealment of early degradation data. These challenges make degradation modeling methods based on traditional data difficult to apply in engineering practice. To address these issues, this paper proposes a novel degradation modeling framework based on physics-informed neural networks. This comprises two modules: feature extraction of physics mechanisms and degradation feature extraction. The former employs a Bayesian causal attention mechanism to extract key physical causal features, capturing the coupled interaction effects among multiple components. The latter utilizes temporal convolutional networks to capture degradation state features, extracting subtle deterioration characteristics from time-series data. These are then fused into a joint representation and embedded in a nonlinear Wiener process to characterize complex degradation dynamics. For enhanced trainability and physical consistency, the Wiener equation is discretized using the Milstein method, and a physics residual regularization term is incorporated into the loss function. During online deployment, a two-stage update strategy is employed to dynamically refine model priors. Experimental validation based on an aerospace product dataset demonstrates that the proposed method achieves high prediction accuracy even under limited-sample conditions.

高可靠性设备的性能退化往往涉及挑战，特别是多部件的耦合和早期退化数据的隐藏。这些挑战使得基于传统数据的退化建模方法难以在工程实践中应用。为了解决这些问题，本文提出了一种基于物理信息神经网络的新型退化建模框架。其中包括物理机制特征提取和退化特征提取两个模块。前者采用贝叶斯因果注意机制提取关键物理因果特征，捕捉多组分之间的耦合交互效应。后者利用时间卷积网络捕获退化状态特征，从时间序列数据中提取细微的退化特征。然后将它们融合成一个联合表示，并嵌入到非线性维纳过程中，以表征复杂的退化动力学。为了提高可训练性和物理一致性，采用Milstein方法对Wiener方程进行离散化，并在损失函数中加入物理残差正则化项。在在线部署过程中，采用两阶段更新策略动态优化模型先验。基于航空航天产品数据集的实验验证表明，即使在有限样本条件下，该方法也具有较高的预测精度。

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引用次数: 0

Survivability-oriented before-event protection resources allocation for multi-target considering uncertain attack resources and attackers’ preferences 考虑不确定攻击资源和攻击者偏好的面向生存能力的多目标事前保护资源分配

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-25 DOI: 10.1016/j.ress.2026.112286

Jingru Zhang , Zhigeng Fang , Cuiping Niu , Chengjie Zhang

This paper aims to address the problem on how to defend critical infrastructures (CIs) against intentional attackers, that is the multi-target defense(MTD) problem. Because the decision makers can hardly ever have precise knowledge on the offensive resource prior to the attack, this paper models the uncertainty surrounding the defender’s knowledge of potential attacks in real-world scenarios. Therefore, a comprehensive defense strategy selection and resource allocation framework is presented. Facing attackers with multiple attack types, uncertain attack resources and different preference weights on targets, CIs aim to maximize the survivability by pre-allocating defensive resources. This framework consists of two stages: the first stage is to determine defense options (individual defense or cooperative defense), and the second stage is outputting optimal resource allocation results. The numerical experiment shows the effectiveness of the established framework. Furthermore, the sensitivity analysis of the number of attack type, attackers’ preferences for targets are analyzed to provide meaningful insights on the defense decisions.

本文旨在解决如何保护关键基础设施免受故意攻击者的攻击，即多目标防御（MTD）问题。由于决策者在攻击之前几乎不可能对进攻资源有精确的了解，因此本文对真实场景中防御者对潜在攻击的不确定性进行了建模。在此基础上，提出了一种综合防御策略选择与资源配置框架。面对攻击类型多、攻击资源不确定、攻击目标优先权重不同的攻击者，攻击策略通过预分配防御资源来实现生存能力的最大化。该框架包括两个阶段：第一阶段是确定防御选择（单独防御还是合作防御），第二阶段是输出最优资源配置结果。数值实验表明了所建立框架的有效性。此外，对攻击类型的数量、攻击者对目标的偏好进行敏感性分析，为防御决策提供有意义的见解。

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引用次数: 0

Component cascade utilization optimization based on an integrated framework with hybrid crack growth prediction 基于混合裂纹扩展预测集成框架的构件级联利用优化

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-25 DOI: 10.1016/j.ress.2026.112275

Jiaoyang Ma, Shuguang He

In complex engineering systems, critical components progressively degrade under high-load conditions, increasing failure risks and maintenance costs. Cascade utilization, which reallocates components from high- to low-load service, offers a cost-effective strategy to extend useful life, with key decisions such as switching time and degradation thresholds relying on accurate remaining useful life (RUL) prediction and risk assessment. This paper proposes an integrated closed-loop framework in which fatigue crack growth prediction directly informs cascade utilization decisions. The Paris law is structurally embedded into the parameters of a Dynamic Gamma-Gamma process, yielding a hybrid crack growth model that coherently integrates deterministic fracture mechanics with stochastic degradation variability. Particle filtering (PF) enables real-time state and parameter updating for adaptive prognosis. To capture the physical hierarchy of degradation, a structured decision-making framework is implemented through a two-stage model: Stage I determines the optimal switching time, while Stage II optimizes degradation thresholds conditional on the health state inherited from Stage I. Numerical experiments and sensitivity analyses demonstrate that the proposed approach improves modeling accuracy and supports adaptive, risk-aware cascade utilization through predictive-prognostic integration.

在复杂的工程系统中，关键部件在高负载条件下逐渐退化，增加了故障风险和维护成本。级联利用将组件从高负载服务重新分配到低负载服务，提供了一种经济有效的延长使用寿命的策略，关键决策如切换时间和退化阈值依赖于准确的剩余使用寿命（RUL）预测和风险评估。本文提出了一个集成的闭环框架，其中疲劳裂纹扩展预测直接为梯级利用决策提供信息。Paris定律在结构上嵌入到动态Gamma-Gamma过程的参数中，从而产生混合裂纹扩展模型，该模型将确定性断裂力学与随机退化变异性相结合。粒子滤波（PF）能够实时更新状态和参数，实现自适应预测。为了捕捉退化的物理层次，通过两阶段模型实现结构化决策框架：阶段I确定最佳切换时间，而阶段II根据从阶段I继承的健康状态优化退化阈值。数值实验和敏感性分析表明，所提出的方法提高了建模精度，并通过预测-预后集成支持自适应、风险意识级联利用。

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引用次数: 0

Analyzing power network vulnerability considering spatial heterogeneous demand under extreme heat 考虑空间异质需求的极端高温条件下电网脆弱性分析

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-24 DOI: 10.1016/j.ress.2026.112287

Mijie Du , Peng Guo , Jing Zhao , Enrico Zio

Power networks are facing significant challenges from frequent extreme heat in terms of operational stress and the risk of cascading failures. This paper proposes a vulnerability analysis framework for power networks exposed to extreme heat scenarios, taking into account spatial heterogeneous demand. Based on the complex distribution of power demand and the varying social impacts of service disruptions, we introduce the Service Disruption–Social Vulnerability Index (SD-SVI) to build a spatial demand model at the level of urban functional zones. Using power network and cascading failure modeling, we apply the SD-SVI weighted method to assess the network’s vulnerability. In addition, we model load growth and line failure rates as driven by extreme heat and design a dual-objective optimization model that considers both vulnerability and failure probability. Case study results show that the SD-SVI weighting significantly affects the vulnerability of the power network, with the continued temperature increase due to climate change making the network more vulnerable. Furthermore, analysis of the obtained Pareto front solutions reveals that extreme heat has a nonlinear effect on system vulnerability, and when temperatures exceed 36°C, single or double branch failures dominate the Pareto front of the power network. Based on the “average frequency × inferred vulnerability” composite index, our analysis shows that protecting vulnerable lines can greatly improve network performance and its ability to adapt to multiple extreme heat scenarios. This study provides theoretical insights and practical guidance for power network vulnerability analysis, risk management and climate change adaptation.

从运行压力和级联故障风险方面来看，电网正面临着频繁的极端高温带来的重大挑战。本文提出了一个考虑空间异质性需求的极端高温情景下电网脆弱性分析框架。基于电力需求的复杂分布和服务中断对社会的不同影响，引入服务中断-社会脆弱性指数（SD-SVI），构建城市功能区层面的空间需求模型。利用电网和级联故障模型，应用SD-SVI加权方法对电网脆弱性进行评估。此外，我们建立了极端高温驱动下的负荷增长和线路故障率模型，并设计了一个考虑脆弱性和故障概率的双目标优化模型。案例研究结果表明，SD-SVI权重对电网脆弱性影响显著，气候变化导致的气温持续升高使电网脆弱性加剧。此外，对Pareto前解的分析表明，极端高温对系统脆弱性具有非线性影响，当温度超过36℃时，电网的Pareto前以单支路或双支路故障为主。基于“平均频率×推断脆弱性”综合指数，我们的分析表明，保护脆弱线路可以大大提高网络性能和适应多种极端高温情景的能力。本研究为电网脆弱性分析、风险管理和适应气候变化提供了理论见解和实践指导。

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引用次数: 0

Wildfire risk assessment of nuclear power plant off-site power systems using human-activity–informed localized inputs: A case study of the Kori nuclear power plant 基于人类活动的局域输入对核电厂场外电力系统的野火风险评估：以Kori核电站为例

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-24 DOI: 10.1016/j.ress.2026.112289

Choi Yeonwoo , Eem Seunghyun , Kwag Shinyoung , Park Jinhee

Wildfire represents a significant natural hazard with the potential to disrupt the off-site power system (OPS) of nuclear power plants (NPPs). Their frequency and intensity are expected to increase due to climate change. The loss of OPS resulting from wildfires can critically affect the safety and operational stability of NPPs, highlighting the need for comprehensive risk assessment. This study compares the results of wildfire risk assessments based on conventional, generalized input data derived from regional statistics with those based on detailed data that incorporate human activity characteristics to improve assessment precision. The proposed methodology is applied to the Kori NPP site, considering the wildfire occurrence frequency at the local administrative level, adjustments based on site accessibility, and regional statistics on wildfire duration. Findings from the Kori NPP case study indicate that incorporating these detailed factors can substantially change the estimated annual probability of loss of the OPS; in this case study, the estimate decreased by up to 95% relative to the baseline. Among all factors, regional variation in wildfire frequency was identified as the most influential parameter. This finding emphasizes the importance of spatially specific input data in enhancing the reliability of wildfire risk assessments. The proposed approach helps avoid both overestimation and underestimation of risk, offering practical insights for developing operational strategies and safety policies for NPPs through localized and accurate analysis.

野火是一种严重的自然灾害，有可能破坏核电站的场外电力系统。由于气候变化，它们的频率和强度预计会增加。野火造成的项目事务处损失可能严重影响核电站的安全和运行稳定性，突出表明需要进行全面的风险评估。本研究比较了基于区域统计数据的常规、广义输入数据和基于包含人类活动特征的详细数据的野火风险评估结果，以提高评估精度。提出的方法应用于Kori核电站站点，考虑了当地行政层面的野火发生频率、基于站点可达性的调整以及野火持续时间的区域统计。Kori核电厂方案个案研究的结果表明，纳入这些详细因素可以大大改变项目事务处每年损失的估计概率；在这个案例研究中，相对于基线，估计值最多减少了95%。在所有因子中，野火频率的区域差异是影响最大的参数。这一发现强调了空间特异性输入数据在提高野火风险评估可靠性方面的重要性。所提出的方法有助于避免高估和低估风险，通过本地化和准确的分析，为制定核电站的运营战略和安全政策提供实用的见解。

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引用次数: 0

A Bayesian hierarchical spatio-temporal generalized extreme value modeling for safety analysis from traffic conflicts 基于贝叶斯层次时空广义极值模型的交通冲突安全分析

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-24 DOI: 10.1016/j.ress.2026.112274

Quansheng Yue , Yanyong Guo , Tarek Sayed , Pan Liu , Hao Lyu

Traffic conflicts at urban intersections often exhibit complex spatio-temporal correlations due to network topology and traffic flow dynamics. Ignoring these dependencies can lead to biased risk estimation and ineffective safety management. While Extreme Value Theory (EVT) is a state-of-the-art approach for proactive safety analysis, existing EVT models often overlook these critical spatial and combined spatio-temporal effects. To address this, this study develops a comprehensive Bayesian hierarchical spatio-temporal Generalized Extreme Value (GEV) modeling approach for non-stationary extreme traffic conflicts. A suite of GEV models, including spatial, temporal, and joint spatio-temporal variations, is proposed. The model integrates spatial correlation via conditional autoregressive structure and temporal correlation using a first-order random walk structure. The approach includes the development of two quantitative safety indices: the risk of crash and the return level, critical for dynamic risk assessment and management. Traffic conflict data from 16 urban intersections in Athens were used for empirical analysis. Results show that models incorporating spatial, temporal, or joint spatio-temporal effects significantly outperform the baseline model, reducing the Deviance Information Criterion (DIC) by averages of 238, 138, and 339, respectively. Crucially, the spatio-temporal GEV models provide the best overall fit, underscoring the necessity of jointly accounting for spatial and temporal effects. Finally, validation of the best-fitted model confirms its strong predictive accuracy, with an average difference of only 1.97 between estimated and observed extreme conflict counts, and the estimates consistently falling within the 95% confidence intervals of the observed risky events, thereby supporting its application for robust and dynamic safety management.

城市交叉口交通冲突受网络拓扑结构和交通流动态的影响，往往表现出复杂的时空相关性。忽略这些依赖关系可能导致有偏差的风险估计和无效的安全管理。虽然极值理论（EVT）是一种先进的主动安全分析方法，但现有的EVT模型往往忽略了这些关键的空间和综合时空效应。为了解决这一问题，本研究开发了一种针对非平稳极端交通冲突的综合贝叶斯分层时空广义极值（GEV）建模方法。提出了一套包括空间、时间和联合时空变化在内的GEV模型。该模型通过条件自回归结构整合空间相关性，并使用一阶随机游走结构整合时间相关性。该方法包括制定两个定量安全指标：碰撞风险和回报水平，这对动态风险评估和管理至关重要。本文利用雅典16个城市十字路口的交通冲突数据进行实证分析。结果表明，结合空间、时间或联合时空效应的模型显著优于基线模型，将偏差信息标准（DIC）平均分别降低238、138和339。重要的是，时空GEV模型提供了最佳的整体拟合，强调了联合考虑空间和时间效应的必要性。最后，对最佳拟合模型的验证证实了其较强的预测准确性，估计的极端冲突数与观测到的极端冲突数之间的平均差值仅为1.97，并且估计始终落在观测到的风险事件的95%置信区间内，从而支持其在稳健和动态安全管理中的应用。

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引用次数: 0

Knowledge-data fusion for water supply pipe failure prediction: A hybrid physics-informed and data-driven method 供水管道故障预测的知识数据融合：一种物理信息和数据驱动的混合方法

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-23 DOI: 10.1016/j.ress.2026.112263

Qunfang Hu , Qiang Zhang , Delu Che , Fei Wang , Zongyuan Zhang , Jiahua Zhou

Pipe failure prediction is critical for daily maintenance and asset management of water distribution networks (WDNs). As the mainstream paradigm for pipe failure modeling, data-driven machine learning (ML) methods are limited by the sparsity of operational data in WDNs and lack physical interpretability. This study proposes a hybrid physics-informed and data-driven method integrating mechanical knowledge with operational data to improve pipe failure prediction. The hybrid method adopts the ML model as its primary architecture, under which a mechanical approach is incorporated to embed the structural safety factor of pipes as an extended physical feature into the feature space of the ML model. The proposed hybrid method is applied to predict pipe failures of a large WDN in China. The results demonstrate that the hybrid models deliver superior predictive capacity and cost-effectiveness compared to pure ML models, achieving significant improvements across various evaluation metrics. The extended physical feature plays a crucial role in pipe failure prediction, with its contribution to the model's predictions aligning with established mechanical principles. Additionally, pipes crossing roads at oblique angles and those located at road intersections are more prone to failure. This research provides insights for improving management strategies and resilience in WDNs.

管道故障预测对于供水管网的日常维护和资产管理至关重要。作为管道故障建模的主流范式，数据驱动的机器学习（ML）方法受到wdn中操作数据的稀疏性和缺乏物理可解释性的限制。该研究提出了一种基于物理和数据驱动的混合方法，将机械知识与操作数据相结合，以改进管道失效预测。混合方法以ML模型为主要架构，在此基础上结合力学方法，将管道结构安全系数作为一种扩展的物理特征嵌入到ML模型的特征空间中。将所提出的混合方法应用于国内某大型WDN的管道故障预测。结果表明，与纯ML模型相比，混合模型提供了卓越的预测能力和成本效益，在各种评估指标上取得了显着改进。扩展物理特征在管道失效预测中起着至关重要的作用，它有助于模型预测与已建立的力学原理保持一致。此外，斜角过马路和十字路口的管道更容易发生故障。本研究为改善wdn的管理策略和弹性提供了见解。

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引用次数: 0