Reliability Engineering & System Safety最新文献_第8页

Probabilistic Functionality Loss Rate Model of Bridge Network for Regional Seismic Fragility Analysis 用于区域地震易损性分析的桥梁网概率功能损失率模型

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-31 DOI: 10.1016/j.ress.2025.111299

Jian Zhong , Sien Zhou , Hao Wang

{"title":"Probabilistic Functionality Loss Rate Model of Bridge Network for Regional Seismic Fragility Analysis","authors":"Jian Zhong , Sien Zhou , Hao Wang","doi":"10.1016/j.ress.2025.111299","DOIUrl":"10.1016/j.ress.2025.111299","url":null,"abstract":"<div><div>The evaluation of traffic functionality of bridge networks in post-earthquake is essential for rescue and reconstruction. The previous studies have a limitation of combination with individual bridge seismic performance analysis and network traffic functionality analysis, which brings a great difficulty to rational aseismic decision making at network-level. Therefore, a probabilistic functionality loss rate model (PFLRM) is established for bridge network. For estimating the network functionality loss, a novel index is defined in PFLRM, named functionality loss rate (FLR). Moreover, the assessment method of FLR is also provided accounting for seismic intensity measure (IM), individual bridge parameters and network topology. In addition, for improving the obtaining efficiency of FLR, a quantified model between FLR and IM is established, which has the advantages of applicability, accuracy and efficiency. The precision of proposed PFLRM is compared by Monte Carlo method on twelve benchmark networks. Finally, the proposed model can be applied on a real network for regional seismic fragility analysis to identify important bridges and make repair strategy.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111299"},"PeriodicalIF":9.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144229936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vulnerability assessments of global liner shipping network based on extended coupled map lattices 基于扩展耦合地图格的全球班轮运输网络脆弱性评价

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-31 DOI: 10.1016/j.ress.2025.111306

Keyu Xu , Jiaguo Liu , Zhenfu Li

{"title":"Vulnerability assessments of global liner shipping network based on extended coupled map lattices","authors":"Keyu Xu , Jiaguo Liu , Zhenfu Li","doi":"10.1016/j.ress.2025.111306","DOIUrl":"10.1016/j.ress.2025.111306","url":null,"abstract":"<div><div>Liner shipping has become a vital part of global trade, and understanding its vulnerability characteristics is essential to ensure its stable transport. In this paper, we present the extended Coupled Map Lattice (CML) model to analyze the vulnerability and cascading failures in liner shipping networks (LSN). The model considers the dynamic interactions between nodes from the perspectives of network topology and transport dynamics, and proposes a new allocation mechanism for load transfer after node failures. The paper investigates through simulation studies the effects of various factors such as node capacity, external interference, and coupling strength on network vulnerability. The results indicate that increasing node capacity can mitigate cascading failures but may lead to increased local flow loss. The study emphasizes that the propagation of disturbances is influenced by network structure and transport relationships, and nodes with the highest topological characteristics may not necessarily result in the most severe cascading failures. Furthermore, simulations suggest that certain nodes, even when subjected to minor disturbances, can cause widespread network failures, highlighting the importance of understanding the dynamic propagation of interruptions in maritime liner shipping networks.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111306"},"PeriodicalIF":9.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144229937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corrigendum to “High-cycle fatigue life prediction for fan blades considering aleatory and epistemic uncertainty with random damage” [Reliability Engineering & System Safety Volume 262, October 2025, 111192] “考虑随机损伤的随机和认知不确定性的风扇叶片高周疲劳寿命预测”的勘误表[可靠性工程与系统安全卷262，October 2025, 111192]

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-31 DOI: 10.1016/j.ress.2025.111292

Xiaodong Zhang , Dengyu Wu , Zhenhong Deng , Siqi Wang , Wujun Feng , Huageng Luo , Baoqiang Zhang

引用次数: 0

Leveraging explainable AI for reliable prediction of nuclear power plant severe accident progression 利用可解释的人工智能对核电站严重事故进展进行可靠预测

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-31 DOI: 10.1016/j.ress.2025.111307

Semin Joo , Yeonha Lee , Seok Ho Song , Kyusang Song , Mi Ro Seo , Sung Joong Kim , Jeong Ik Lee

{"title":"Leveraging explainable AI for reliable prediction of nuclear power plant severe accident progression","authors":"Semin Joo , Yeonha Lee , Seok Ho Song , Kyusang Song , Mi Ro Seo , Sung Joong Kim , Jeong Ik Lee","doi":"10.1016/j.ress.2025.111307","DOIUrl":"10.1016/j.ress.2025.111307","url":null,"abstract":"<div><div>Past severe accidents have highlighted the importance of reducing human error by operators in accident situations. To support operators, machine learning-based accident management support tools have been proposed due to its rapid computation and generalization capabilities. However, the lack of explainability in these models, often perceived as \"black-boxes,\" remains a significant challenge. To address this issue, Explainable AI (XAI) techniques are being integrated across various domains. This study evaluates the applicability of XAI techniques in predicting the state of the OPR1000 reactor during a subset scenario of total-loss-of-component-cooling-water accident with dynamic random failure assumption. Accident scenarios, including various safety component failures and mitigation strategies, were simulated using the Modular Accident Analysis Program (MAAP) code. Two types of XAI techniques—Shapley Additive Explanations (SHAP) and attention-based architectures—are tested alongside conventional black-box models. The results demonstrate that relationships among thermal-hydraulic variables can be explained via feature importance, and that the impacts of component failures and mitigation strategies are phenomenologically explainable. Additionally, the study highlights the importance of robust, domain knowledge-based data engineering.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111307"},"PeriodicalIF":9.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging data mining for critical branch identification through simultaneity and causality correlation analysis under cascading failures in power systems 利用数据挖掘技术对电力系统级联故障下的关键支路进行同时性和因果关联分析

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-31 DOI: 10.1016/j.ress.2025.111298

Ziran Gao , Mahesh Illindala , Jieyu Lei

{"title":"Leveraging data mining for critical branch identification through simultaneity and causality correlation analysis under cascading failures in power systems","authors":"Ziran Gao , Mahesh Illindala , Jieyu Lei","doi":"10.1016/j.ress.2025.111298","DOIUrl":"10.1016/j.ress.2025.111298","url":null,"abstract":"<div><div>Identifying critical branches or propagation paths from cascading failure data can be an effective way to mitigate and even prevent cascading blackouts in power systems. Hence, this paper proposes a data mining-based identification framework to find critical correlations among propagating pathways. We define simultaneity correlation and causality correlation to comprehensively reveal the fault propagation features according to the temporal and synchronous dependence of critical branches during fault propagation. The itemset and sequence mining pattern is used to model the two types of correlations and then mine the critical correlations, respectively. To reduce the impacts of the incompleteness of initial conditions and improve the accuracy of mining, the Shannon diversity index is introduced to quantify the diversity of initial conditions. Moreover, we propose a recursion graph-based probability calculation model to fast predict the probability/risk of occurrence of the unknown correlations in cascading failures. Numerical simulation results based on the IEEE 118-bus system verify the effectiveness of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111298"},"PeriodicalIF":9.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A covariate-dependent Markov jump process with application to the propagation of rail defect severity 一个协变量相关的马尔可夫跳跃过程及其在钢轨缺陷严重程度传播中的应用

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-31 DOI: 10.1016/j.ress.2025.111257

Albert Skovgaard Bisgaard , Steven Harrod , Jan Kloppenborg Møller , Bo Friis Nielsen , Carsten Jørn Rasmussen , Thomas Vatn Bjørge , Jørn Vatn

{"title":"A covariate-dependent Markov jump process with application to the propagation of rail defect severity","authors":"Albert Skovgaard Bisgaard , Steven Harrod , Jan Kloppenborg Møller , Bo Friis Nielsen , Carsten Jørn Rasmussen , Thomas Vatn Bjørge , Jørn Vatn","doi":"10.1016/j.ress.2025.111257","DOIUrl":"10.1016/j.ress.2025.111257","url":null,"abstract":"<div><div>Rail defects pose a significant threat to railway safety and efficiency. Refined modeling of the propagation of rail defect severity has the potential of informing maintenance activities for circumvention of dangerous rail degradation. We consider discretely observed degradation trajectories for defects discovered on the Norwegian rail network with the impact from tonnage and line speed, as well as rail curvature, profile and grade. The propagation of defect severity is modeled using a continuous-time Markov chain regressed on covariates. We propose two estimation approaches: (1) direct maximization of the discrete data log-likelihood using analytical gradient information, and (2) Monte Carlo simulation of fully observed defect trajectories, which informs an Expectation–Maximization algorithm Both methodologies give rise to fast convergence of model estimates with similar estimates, indicating a favorable local optimum. The covariate parameters are statistically significant and align with their expected physical effects. Model checking is performed by cross-validation. Experiments with indicator variables demonstrate that the included exogenous information satisfactorily accounts for the structural variability between the different rail lines. We compute expected transition times for defects on selected rail lines and demonstrate how spatially varying track conditions affect defect propagation.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111257"},"PeriodicalIF":9.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144239470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incipient fault detection based on double Kullback–Leibler divergence KLDattW improved by a self-attention mechanism 基于自关注机制改进的双Kullback-Leibler散度KLDattW的早期故障检测

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-31 DOI: 10.1016/j.ress.2025.111247

Yiming Tang , Pengfei Ma , Lei Li , Xin Liu , Yanjun Liu , Qiuliang Wang

{"title":"Incipient fault detection based on double Kullback–Leibler divergence KLDattW improved by a self-attention mechanism","authors":"Yiming Tang , Pengfei Ma , Lei Li , Xin Liu , Yanjun Liu , Qiuliang Wang","doi":"10.1016/j.ress.2025.111247","DOIUrl":"10.1016/j.ress.2025.111247","url":null,"abstract":"<div><div>The precise identification of incipient faults in industrial processes presented a significant challenge, as traditional methods based on principal component analysis (PCA) exhibit unsatisfactory detection rates. Kullback–Leibler divergence (KLD) detection improves fault detection capabilities to a certain extent, but it processes all the statistical components in the same way: diminishing or obscuring essential data that are pertinent to faults. This paper presents a self-attention-based double KLD detection technique in which the first stage of KLD is combined with the local outlier factor (LOF) to quantify the severity of faults. The second KLD stage calculates a new statistic, <span><math><mrow><mi>K</mi><mi>L</mi><msubsup><mrow><mi>D</mi></mrow><mrow><mi>a</mi><mi>t</mi><mi>t</mi></mrow><mrow><mi>W</mi></mrow></msubsup></mrow></math></span>, on the basis of the fault-weighted scores obtained from the self-attention mechanism. Additionally, control limits are determined via the kernel density estimation (KDE) method. <span><math><mrow><mi>K</mi><mi>L</mi><msubsup><mrow><mi>D</mi></mrow><mrow><mi>a</mi><mi>t</mi><mi>t</mi></mrow><mrow><mi>W</mi></mrow></msubsup></mrow></math></span> validated this method by applying it to three types of incipient sensor faults induced during the continuous stirred tank heater (CSTH) process and two incipient faults induced during the Tennessee Eastman (TE) process, demonstrating its superior fault detection rates (FDRs) and efficacy compared to the existing methods in all evaluated cases.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111247"},"PeriodicalIF":9.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unleashing data power: Driving maritime risk analysis with Bayesian networks 释放数据力量：用贝叶斯网络驱动海事风险分析

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-31 DOI: 10.1016/j.ress.2025.111310

Jiaxin Wang, Hanwen Fan, Zheng Chang, Jing Lyu

{"title":"Unleashing data power: Driving maritime risk analysis with Bayesian networks","authors":"Jiaxin Wang, Hanwen Fan, Zheng Chang, Jing Lyu","doi":"10.1016/j.ress.2025.111310","DOIUrl":"10.1016/j.ress.2025.111310","url":null,"abstract":"<div><div>With the rapid growth of global shipping, increasing maritime traffic has heightened accident risks, posing threats to the economy, ecology, and public safety. This study introduces a data-driven Bayesian network (BN) framework to identify key risk factors for incident severity, considering data deficiencies. Firstly, boxplot techniques and the Adaptive Synthetic Sampling algorithm are introduced to handle outliers and imbalanced data, thereby supporting a valid dataset for model construction. Then, this study introduces the AcciMap theory, which provides a more comprehensive representation of accident causation from complex sociotechnical systems perspectives. Meanwhile, the K-means clustering method is employed to effectively overcome the high subjectivity inherent in traditional indicator state classification. Finally, we propose techniques to assess the framework performance and validate our framework. Our findings reveal: (1) “Standardized Operations” are identified as the key influential factor on maritime accidents, with a mutual information value of 0.134; (2) Human behavioral norms gain importance as incident severity increases; (3) Scenario analysis highlights that favorable weather conditions can paradoxically lead to more severe accidents. This study offers valuable insights for policymakers and industry practitioners, providing a robust framework for maritime risk management and accident prevention.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111310"},"PeriodicalIF":9.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic inoperability input-output modeling of a system of systems made of multi-state interdependent critical infrastructures 由多状态相互依赖的关键基础设施组成的系统的动态不可操作性输入-输出建模

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-30 DOI: 10.1016/j.ress.2025.111303

Maria Valentina Clavijo Mesa , Francesco Di Maio , Enrico Zio

引用次数: 0

Joint reliability of linear two-dimensional consecutive k-type systems with shared components 具有共享构件的线性二维连续k型系统的联合可靠性

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-29 DOI: 10.1016/j.ress.2025.111193

He Yi , Narayanaswamy Balakrishnan , Xiang Li

引用次数: 0