Reliability Engineering & System Safety最新文献

Optimizing port system resilience through integrated preparedness and recovery strategies 通过综合准备和恢复战略优化港口系统弹性

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-10-01 DOI: 10.1016/j.ress.2025.111770

Zheng Xing , Chenhao Zhou , Yu Shen , Ek Peng Chew , Kok Choon Tan

{"title":"Optimizing port system resilience through integrated preparedness and recovery strategies","authors":"Zheng Xing , Chenhao Zhou , Yu Shen , Ek Peng Chew , Kok Choon Tan","doi":"10.1016/j.ress.2025.111770","DOIUrl":"10.1016/j.ress.2025.111770","url":null,"abstract":"<div><div>Ports, recognized as intricate systems, are susceptible to a variety of human-induced incidents and natural phenomena that can result in disruptions. Strengthening the port’s ability to manage disruptions and bolstering the resilience of the port system play a crucial role in ensuring the smooth operation of commercial trade. Nevertheless, assessing the port’s resilience and making decisions regarding pre- and post-disruption actions in uncertain circumstances present notable challenges. This research delves into the topic of network resilience within port logistics and operational infrastructure, introducing a novel indicator for evaluating port resilience. Moreover, the study frames this issue as a stochastic mixed-integer linear programming (SMILP), determining preparedness and recovery measures to enhance the resilience of the port system. Subsequently, a double-decomposed methodology is suggested for resolving the model, which incorporates Lagrangian Decomposition and a branch-and-price algorithm utilizing Dantzig–Wolfe Decomposition. Ultimately, the efficacy of the algorithm and the significance of the strategies in risk management are validated through a practical case study.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111770"},"PeriodicalIF":11.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267751","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

Resilient maintenance of carbonation-affected concrete infrastructure via physics-informed learning and predictive strategy 通过物理信息学习和预测策略对受碳化影响的混凝土基础设施进行弹性维护

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-10-01 DOI: 10.1016/j.ress.2025.111761

Chunhui Guo, Zhenglin Liang

{"title":"Resilient maintenance of carbonation-affected concrete infrastructure via physics-informed learning and predictive strategy","authors":"Chunhui Guo, Zhenglin Liang","doi":"10.1016/j.ress.2025.111761","DOIUrl":"10.1016/j.ress.2025.111761","url":null,"abstract":"<div><div>Climate change accelerates carbonation in concrete, raising risks of cracking and spalling. However, existing model formulations often oversimplify this impact, inadequately representing the intrinsically non-linear and phase-dependent behavior of the carbonation process. In this paper, we propose a novel framework that integrates Physics-Informed Neural Networks (PINNs) and a Predictive Markov Decision Process with Phase-type Approximation (PMDP-PH), enabling resilient and cost-effective infrastructure maintenance under carbonation risks. PINNs embed governing physical laws within neural architectures, enabling accurate inference of carbonation dynamics even under limited observational data. This framework accommodates non-exponential sojourn time distributions in both the initiation and propagation phases, effectively approximated using hypo-exponential models. The PMDP-PH adaptively updates inspection and maintenance strategies by continuously refining the remaining useful life (RUL) distribution in real time. This decision-making process is formulated as a tractable multi-stage model predictive control (MPC) problem over selected belief states, ensuring a non-decreasing value function throughout the robust horizon. Applied to a representative infrastructure system, our method reduces total maintenance costs by up to 61.9% compared to benchmark strategies under variable deterioration scenarios. These findings highlight the promise of combining physics-informed learning with a new form of predictive control strategy to strengthen infrastructure resilience under the growing threat of carbonation-induced deterioration.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111761"},"PeriodicalIF":11.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221132","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 dimensional reduction outcrossing rate method for time-dependent system reliability analysis 时变系统可靠性分析的降维异交率方法

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-09-30 DOI: 10.1016/j.ress.2025.111772

Hang Lin, Xuan-Yi Zhang, Yan-Gang Zhao

{"title":"A dimensional reduction outcrossing rate method for time-dependent system reliability analysis","authors":"Hang Lin, Xuan-Yi Zhang, Yan-Gang Zhao","doi":"10.1016/j.ress.2025.111772","DOIUrl":"10.1016/j.ress.2025.111772","url":null,"abstract":"<div><div>Reliability assessment of engineering structures requires accounting for the stochastic nature of loads and material properties, as well as the interaction among multiple components. These characteristics make time-dependent system reliability (TSR) a critical yet challenging problem. The outcrossing rate method has been introduced for TSR analysis, while its extension to multi-component systems suffers from exponential growth in the number of sub-events and high-dimensional computations. To address these challenges, a dimensional reduction outcrossing rate (DRO) method is proposed for efficient TSR analysis of both series and parallel systems. To address the challenge induced by the multiple outcrossing events, a theoretical model for the outcrossing rate is proposed, expressed in terms of two sub-events. An efficient numerical method is developed to compute the probability of these two events. Specifically, the instantaneous reliability indices and the correlation matrix of all component failure events are evaluated in a reduced-dimensional random space, which further improve the efficiency. Additionally, the failure probability, defined by a multiple integral, is computed using a dimensional reduction technique. The accuracy and efficiency of the DRO method are demonstrated through four case studies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111772"},"PeriodicalIF":11.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267827","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

Robust condition-based generation maintenance: Balancing operations and start/stop cycling to control asset degradation rates 稳健的基于状态的发电维护：平衡操作和启动/停止循环，以控制资产劣化率

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-09-30 DOI: 10.1016/j.ress.2025.111776

Deniz Altinpulluk, Murat Yildirim

{"title":"Robust condition-based generation maintenance: Balancing operations and start/stop cycling to control asset degradation rates","authors":"Deniz Altinpulluk, Murat Yildirim","doi":"10.1016/j.ress.2025.111776","DOIUrl":"10.1016/j.ress.2025.111776","url":null,"abstract":"<div><div>The integration of renewable energy, distributed generation, and electric vehicle charging into modern power grids has created a highly variable operational environment. Current operations and maintenance models lack the flexibility to accommodate this increased variability and its effects on degradation, leading to more frequent start/stop cycles that significantly impact asset lifespans. In this paper, we propose a robust optimization framework designed to optimize generation maintenance schedules and unit commitment decisions in power systems. Our approach explicitly models the impact of start/stop cycling on remaining useful lifetime distributions, providing accurate failure risk assessments. By incorporating sensor-informed and decision-dependent degradation models within an operations and maintenance optimization model, our framework effectively balances lifetime utilization, failure risks, and operational efficiency. We validate the effectiveness of our framework through computational experiments using real-world degradation signals, demonstrating its advantages over the benchmark models.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111776"},"PeriodicalIF":11.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267832","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

Temporal modeling and resilience analysis of supply chain networks under cascading failures 级联故障下供应链网络的时间建模与弹性分析

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-09-29 DOI: 10.1016/j.ress.2025.111763

Junwei Shi, Zhejia Tang, Xiu-Xiu Zhan, Chuang Liu

{"title":"Temporal modeling and resilience analysis of supply chain networks under cascading failures","authors":"Junwei Shi, Zhejia Tang, Xiu-Xiu Zhan, Chuang Liu","doi":"10.1016/j.ress.2025.111763","DOIUrl":"10.1016/j.ress.2025.111763","url":null,"abstract":"<div><div>Enhancing supply chain resilience amid global disruptions remains a pressing challenge. While prior research has largely focused on static assessments that emphasize system degradation, the dynamic recovery and reconfiguration processes of Supply Chain Networks (SCNs) are often overlooked. To bridge this gap, we propose a Temporal Supply Chain Network (TSCN) model that captures key temporal dynamics, including firm entry and exit, relationship turnover, and operational state transitions. To simulate disruption propagation and recovery, we develop a cascading failure model driven by underload mechanisms and incorporate node-level recovery dynamics. Furthermore, we introduce a time-dependent resilience metric based on node reachability over adjustable time windows, enabling a granular assessment of network functionality restoration. Through simulations on both synthetic TSCNs and empirical data from the temporal global wheat trade network, we evaluate resilience under random and targeted disruptions. Our findings reveal that resilience is governed not only by static topology but also by the evolving interplay of node capacities, load distributions, and recovery potentials. The proposed framework offers a dynamic perspective on SCNs resilience, providing actionable insights for designing more adaptive and robust supply chain systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111763"},"PeriodicalIF":11.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221135","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

Dimension reduction for efficient Bayesian inference of high-dimensional quantity of interest problems with parametric and nonparametric uncertainties 具有参数和非参数不确定性的高维数量感兴趣问题的有效贝叶斯推理降维

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-09-29 DOI: 10.1016/j.ress.2025.111764

Xiaoshu Zeng , Roger Ghanem , Bora Gencturk , Olivier Ezvan

{"title":"Dimension reduction for efficient Bayesian inference of high-dimensional quantity of interest problems with parametric and nonparametric uncertainties","authors":"Xiaoshu Zeng , Roger Ghanem , Bora Gencturk , Olivier Ezvan","doi":"10.1016/j.ress.2025.111764","DOIUrl":"10.1016/j.ress.2025.111764","url":null,"abstract":"<div><div>This paper addresses the challenges of efficient Bayesian inverse analysis for high-dimensional parameter spaces and quantities of interest (QoIs), such as output fields. Two main challenges are identified: (i) the need for numerous forward model evaluations during posterior sampling, and (ii) the exploration of the high-dimensional parameter space. To address the first challenge, a probabilistic surrogate model based on polynomial chaos expansions (PCE) is proposed. However, PCE for high-dimensional parameter spaces faces difficulties in robust uncertainty quantification. Although basis adaptation in PCE is promising for low-dimensional QoIs, it struggles with high-dimensional output fields and convergence issues. Additionally, modeling errors introduce further uncertainties.</div><div>To overcome these challenges, an integrated approach employing dimension reduction techniques for both the QoI and parameter space is introduced. For the QoI, a truncated Karhunen-Loève expansion (KLE) is used, and for the parameter space, basis adaptation with convergence acceleration algorithms is applied. This results in a surrogate model that replaces the physical model, significantly improving computational efficiency. To account for uncertainties due to modeling errors, a nonparametric stochastic approach is incorporated into the surrogate model. For the second challenge in Bayesian inference, a block-update Markov Chain Monte Carlo (MCMC) algorithm is applied to promote mixingand enhance the acceptance rate of posterior sampling. The effectiveness of the methods is validated through detailed cases of boiling water reactor spent nuclear fuel assemblies and fully-loaded spent nuclear fuel canisters, demonstrating the applicability and efficiency of the integrated surrogate modeling and block-update MCMC for high-dimensional problems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111764"},"PeriodicalIF":11.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267742","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

Joint optimization of maintenance and resource preparation of system with multi-indicator degradation based on multiple failure mode division 基于多失效模式划分的多指标退化系统维护与资源准备联合优化

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-09-29 DOI: 10.1016/j.ress.2025.111768

Xiaohong Zhang , Yongfei Zhang , Guannan Shi , Jie Gan , Jinpeng Liu , Anshu Dai

{"title":"Joint optimization of maintenance and resource preparation of system with multi-indicator degradation based on multiple failure mode division","authors":"Xiaohong Zhang , Yongfei Zhang , Guannan Shi , Jie Gan , Jinpeng Liu , Anshu Dai","doi":"10.1016/j.ress.2025.111768","DOIUrl":"10.1016/j.ress.2025.111768","url":null,"abstract":"<div><div>With the increasing complexity of industrial systems, such as rolling mills, fluids, and pavements often require multiple indicators to reflect the health state. Multiple indicators correspond to multiple fault types, and multiple fault types further correspond to multiple maintenance types, resulting in multiple maintenance times, costs, effects, and demands on maintenance resources. This research focuses on the joint optimization of maintenance and resource preparation for systems with multi-indicator degradation based on multiple failure mode division. Firstly, the joint degradation modeling is conducted based on Copula model, and multiple fault types are defined based on the multiple failure mode division model for the system with two indicator degradation. Secondly, a joint strategy of maintenance and resource preparation is developed. Then, the cost rate model is established to determine the optimal inspection period and resource preparation thresholds, and a probability model of combined state is proposed recursively for relevant probability solutions. Finally, the correctness and efficiency of the model are verified by taking the mill rolls as a case study. The results show that the joint strategy can reduce the maintenance cost compared the strategy of preparing maintenance resources after system fault.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111768"},"PeriodicalIF":11.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267830","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

Knowledge regroupment and preference calibration framework for unpredicted fault diagnosis under unknown working conditions 未知工况下不可预测故障诊断的知识重组和偏好校准框架

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-09-27 DOI: 10.1016/j.ress.2025.111775

Qiuyu Song , Xingxing Jiang , Shang-kuo Yang , He Ren , Jie Liu , Zhongkui Zhu

{"title":"Knowledge regroupment and preference calibration framework for unpredicted fault diagnosis under unknown working conditions","authors":"Qiuyu Song , Xingxing Jiang , Shang-kuo Yang , He Ren , Jie Liu , Zhongkui Zhu","doi":"10.1016/j.ress.2025.111775","DOIUrl":"10.1016/j.ress.2025.111775","url":null,"abstract":"<div><div>In engineering scenarios, the absence of target domain data during model training phase leads to uncertainties in the encountered faults and working conditions. Identifying unpredicted faults including known and unknown classes under new working conditions poses a realistic and great challenge for reliability of mechanical system. The challenge is further intensified by inconsistent fault label space among multi-source domains. Current intelligent diagnostic models will be terribly powerless on such real-time diagnostic situation of open set domain generalization with category shift among multi-source domains. Therefore, to overcome this extremely challenging issue in practice, a knowledge regroupment and preference calibration framework (KRPCF) is established in this study. A knowledge regroupment scheme for generalizable feature learning and a categorical preference calibration loss for open set fault classifier training are innovatively designed in KRPCF to simultaneously solve domain shift among domains, category shift among source domains, and category shift between source domains and unseen target domains. In comprehensive experimental results based on various performance evaluations, average accuracy and average H-score of KRPCF surpass the best baseline method by more than 4.6% and 8.1%, respectively, which demonstrates the strong potential of KRPCF in practical applications. In-depth discussion on the visualized preferences of the open set fault classifier and the robustness of KRPCF demonstrates its reliable unpredicted fault diagnosis under open set domain generalization with category shift among source domains.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111775"},"PeriodicalIF":11.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221133","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

Dependence-aware modeling of multi-tenant attacks in cloud systems 云系统中多租户攻击的依赖感知建模

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-09-27 DOI: 10.1016/j.ress.2025.111750

Yijia Li , Maochao Xu , Peng Zhao

{"title":"Dependence-aware modeling of multi-tenant attacks in cloud systems","authors":"Yijia Li , Maochao Xu , Peng Zhao","doi":"10.1016/j.ress.2025.111750","DOIUrl":"10.1016/j.ress.2025.111750","url":null,"abstract":"<div><div>Multi-tenant cloud systems are increasingly vulnerable to co-residency attacks, in which adversaries deploy attacker virtual machines (AVMs) to compromise service component versions (SCVs) colocated on shared physical servers. Conventional reliability models often assume independent SCV failures, overlooking dependencies arising from shared vulnerabilities or coordinated attacks. We introduce a dependence-aware probabilistic framework that explicitly models statistical dependence among SCV compromises via copula-based joint distributions, and incorporates various AVM placement policies (random, hash, affinity). We analyze how SCV dependence structure, the number of attacker accounts, and IDS detection sensitivity affect the overall corruption probability. The risk model is further embedded in a Stackelberg game between defender and attacker, incorporating budget and risk-cap constraints and various detection cost regimes. We prove equilibrium existence and compute optimal strategies via a Monte Carlo procedure. It is discovered that dependence significantly increases the risk of corruption. The probability of corruption can increase by up to 75% compared to the independence baseline, with non-overlapping confidence intervals across different copula families and placement policies. Equilibrium analysis shows that placement and cost structure jointly determine the optimal detection sensitivity. These results demonstrate how dependence modeling, placement realism, and operational constraints together shape cloud service resilience and defender strategy.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111750"},"PeriodicalIF":11.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267750","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

Exploring metro reliability under synchronous cascading congestion induced by line perturbation 探讨由线路扰动引起的同步级联拥塞下的地铁可靠性

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-09-26 DOI: 10.1016/j.ress.2025.111774

Yi Shen , Gang Ren , Bin Ran

{"title":"Exploring metro reliability under synchronous cascading congestion induced by line perturbation","authors":"Yi Shen , Gang Ren , Bin Ran","doi":"10.1016/j.ress.2025.111774","DOIUrl":"10.1016/j.ress.2025.111774","url":null,"abstract":"<div><div>In metros, line perturbation caused by local accidents can affect all the stations on the same line. Moreover, this impact can spread to other lines through line interaction, leading to network synchronous cascading congestion and bringing great risk to metros. This paper proposes a cascading model to study the synchronous congestion spreading mode and metro reliability under line perturbation. In the model, the station carrying capacity is calculated based on system balance and evolves through congestion spreading, line perturbation, and line interaction, realizing the full coupling of stations on metro line. The passenger behaviour is considered by an impedance function combined with station state and traffic condition. Nanjing metro is studied. The results reveal a multi-point synchronous congestion extension mode of the network under line perturbation and interaction. Moreover, larger line perturbation and interaction can lead to faster congestion spreading and a larger decline of the network reliability, but the congestion risk can gradually decrease with the increase of hops along lines. High station tolerance, turn back mode, and train frequency adjustment of transfer lines and segment lines are beneficial to the network reliability. The optimal train frequency adjustments of transfer lines and segment lines are also obtained by limiting congestion spreading. This paper provides theoretical supports for synchronous dynamics of metros and cascading reliability analysis. The methods and findings can be extended to other networked systems to address the cascading reliability with multi-point synchronization congestion.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111774"},"PeriodicalIF":11.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221134","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