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

Monitoring of operational resilience on urban road network: A Shaoxing case study

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-15 DOI: 10.1016/j.ress.2025.110836

Jianwei Du , Gang Ren , Jialei Cui , Qi Cao , Jian Wang , Chenyang Wu , Jiefei Zhang

{"title":"Monitoring of operational resilience on urban road network: A Shaoxing case study","authors":"Jianwei Du , Gang Ren , Jialei Cui , Qi Cao , Jian Wang , Chenyang Wu , Jiefei Zhang","doi":"10.1016/j.ress.2025.110836","DOIUrl":"10.1016/j.ress.2025.110836","url":null,"abstract":"<div><div>Urban road networks (URNs), which are the critical infrastructure of a city, are fragile when faced with external disruptions. Efficient and accurate analyses of URN resilience of URNs could provide a new perspective for enhancing their ability to withstand, adapt, and recover from disruptive events. This study focused on the resilience evaluation and prediction of URN. A time-varying belief Markov-based resilience model was proposed to analyze the Operational Resilience (OR), which integrates link usability and driving efficiency. The OR is then converted to a normalized scale (COR), which is easier for decision-makers to understand. Finally, a case study was conducted to validate the proposed model. The results showed that demand, link capacity, disaster intensity, and road network structure are significant factors affecting the OR of a URN. Within the OR threshold, the recovery time is generally half the response time and is more stable among different links and precipitation intensities. It has been proven to have satisfactory performance in the estimation and prediction of resilience, which can capture the long-term OR of URN and identify key links and regions that require more attention. This approach could assist decision-makers in developing effective measures for disruptive events.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110836"},"PeriodicalIF":9.4,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143219617","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

Benchmark transformation neural network for health indicator construction under time-varying speed and its application in machinery prognostics

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-15 DOI: 10.1016/j.ress.2025.110823

Jiahong Yang , Jianghong Zhou , Yi Chai , Dingliang Chen , Yi Qin

{"title":"Benchmark transformation neural network for health indicator construction under time-varying speed and its application in machinery prognostics","authors":"Jiahong Yang , Jianghong Zhou , Yi Chai , Dingliang Chen , Yi Qin","doi":"10.1016/j.ress.2025.110823","DOIUrl":"10.1016/j.ress.2025.110823","url":null,"abstract":"<div><div>A health indicator (HI) is usually applied to predict the remaining useful life (RUL) of machinery. The current HI construction methods typically only focus on constant operating conditions (such as speed or load), rendering them ineffective for variable-speed conditions. To address this gap, this paper proposes a HI construction method for machines under time-varying speed conditions based on benchmark transformation neural networks (BTNN). First, the baseline speed range is determined, and the baseline state observations are identified. Then, with the identified baseline observations, a performance degradation model is established via the double exponential function. Next, BTNN is innovatively proposed using the fitted degradation model, the monitoring state, and speed data to adaptively perform the complex nonlinear mapping from non-baseline observations to baseline values, avoiding the problem of selecting different baseline transformation functions. Compared with the HI constructed by a transformation function and the dimensionless HI, the proposed method unifies state observations at various speeds onto the baseline speed through BTNN, enhancing the comprehensive performance of HI. Comparative experiments on the RUL prediction of turbofan engines and wind turbine bearings reveal that the HI extracted by BTNN exhibits stronger prognosis capabilities than other classical and advanced HIs.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110823"},"PeriodicalIF":9.4,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143286448","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

Attack strategies and reliability analysis of Wireless Mesh Networks considering cascading failures

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-15 DOI: 10.1016/j.ress.2025.110832

Hongyan Dui , Jiabao Zhai , Xiuwen Fu

{"title":"Attack strategies and reliability analysis of Wireless Mesh Networks considering cascading failures","authors":"Hongyan Dui , Jiabao Zhai , Xiuwen Fu","doi":"10.1016/j.ress.2025.110832","DOIUrl":"10.1016/j.ress.2025.110832","url":null,"abstract":"<div><div>Wireless Mesh Networks (WMNs) are vital to modern infrastructure, and attacks on WMNs can lead to paralysis of interdependent systems, causing significant social and economic consequences. However, existing studies often assume all attacks coincide, focusing on cascading failures from a single perspective while neglecting specific attack strategies. This study is the first to apply sequential attack strategies to WMNs and conduct a comparative analysis with synchronous strategies. First, we propose a system model for WMNs that incorporates path loss and link capacity, representing real-world scenarios. Secondly, we introduce two novel reliability metrics, transmission reliability and reliability degradation rate, to quantify the impact of attacks. In the final case study, we analyze the effects of synchronous and sequential attack strategies under four node attack rules. Results reveal that sequential attack strategies dynamically identify and exploit critical nodes, causing prolonged degradation of transmission reliability and amplifying cascading failures. Compared to synchronous attack strategies, sequential attack strategies increase the global reliability degradation rate and reliability degradation rate during the attack phase by 28.62 % and 55.36 %, respectively. Therefore, sequential attack strategies should combine these two node attack rules to achieve maximum impact. These findings confirm that the proposed sequential attack strategy effectively disrupts network performance and provides meaningful insights for developing WMN defense strategies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110832"},"PeriodicalIF":9.4,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143286635","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

Condition-based maintenance for redundant systems considering spare inventory with stochastic lead time

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-15 DOI: 10.1016/j.ress.2025.110837

Jun Wang , Yuqiang Fu , Jian Zhou , Lechang Yang , Yating Yang

{"title":"Condition-based maintenance for redundant systems considering spare inventory with stochastic lead time","authors":"Jun Wang , Yuqiang Fu , Jian Zhou , Lechang Yang , Yating Yang","doi":"10.1016/j.ress.2025.110837","DOIUrl":"10.1016/j.ress.2025.110837","url":null,"abstract":"<div><div>Condition-based maintenance (CBM) and spare provisioning are both important to guarantee the operation of redundant systems composed of degrading components. However, most existing studies on joint optimization of CBM and spare inventory assume maintenance actions are instantaneous and the lead time for spares are fixed, which are not consistent with the reality. Therefore, this paper focus on the joint optimization problems to minimize the total cost rate considering stochastic maintenance time for components and stochastic lead time for spares. The problem is modeled as a Markov decision process model and solved by an improved reinforcement learning algorithm, i.e., the improved Q-learning algorithm, which converges more quickly and reaches a smaller value of the total cost rate than the traditional Q-learning algorithm. Moreover, the simulated environment based on discrete event simulation method is introduced in detail and the convergence of the algorithm is proved theoretically. Based on the numerical study, we further demonstrate the convergence and effectiveness of the proposed algorithm and perform sensitivity analysis on several model parameters to provide management insights for decision makers.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110837"},"PeriodicalIF":9.4,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143286642","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

Risk influencing factors on the consequence of waterborne transportation accidents in China (2013–2023) based on data-driven machine learning

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-14 DOI: 10.1016/j.ress.2025.110829

Weiliang Qiao , Enze Huang , Meng Zhang , Xiaoxue Ma , Dong Liu

{"title":"Risk influencing factors on the consequence of waterborne transportation accidents in China (2013–2023) based on data-driven machine learning","authors":"Weiliang Qiao , Enze Huang , Meng Zhang , Xiaoxue Ma , Dong Liu","doi":"10.1016/j.ress.2025.110829","DOIUrl":"10.1016/j.ress.2025.110829","url":null,"abstract":"<div><div>Early warning on the basis of RIFs data is widely considered as an effective way to prevent waterborne transportation accidents, and the performance of warning model is critical. To develop a warning model with good performance, in this study, a data-driven based comprehensive machine learning algorithm, namely BiLSTM-CNN-RF is proposed. The RIFs data used to train the proposed algorithm is extracted from the 1090 waterborne transportation accident investigation reports during 2013–2023 in China, the collected data is first pre-processed to establish the input sample set of the algorithms. Meanwhile the importance of RIFs is also quantitatively analyzed. The traditional machine learning algorithms, such as RF, SVM, MPL, and GRU, are also involved in this study to verify the performance of the proposed comprehensive algorithm. The RIFs data is then fed into these five machine learning algorithms, the prediction results of “Accident type” and “Accident grade” are used to examine their prediction performance. The results show that the performance of the proposed BiLSTM-CNN-RF algorithm is better than the four traditional machine learning algorithms, especially for prediction accuracy, and another superiority is the good applicability in case of small sample data volume.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110829"},"PeriodicalIF":9.4,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143219605","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 “Risk assessment of main accident causes at highway-rail grade crossings” [Reliability Engineering & System Safety Volume 256 (2025) 110764]

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-14 DOI: 10.1016/j.ress.2025.110826

Xiyuan Chen , Xiaoping Ma , Limin Jia , Fei Chen

引用次数: 0

A novel hybrid approach combining PDEM and bayesian optimization deep learning for stochastic vibration analysis in train-track-bridge coupled system

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-14 DOI: 10.1016/j.ress.2025.110827

Jianfeng Mao , Zheng Li , Zhiwu Yu , Lianjun Hu , Mansoor Khan , Jun Wu

{"title":"A novel hybrid approach combining PDEM and bayesian optimization deep learning for stochastic vibration analysis in train-track-bridge coupled system","authors":"Jianfeng Mao , Zheng Li , Zhiwu Yu , Lianjun Hu , Mansoor Khan , Jun Wu","doi":"10.1016/j.ress.2025.110827","DOIUrl":"10.1016/j.ress.2025.110827","url":null,"abstract":"<div><div>Train-track-bridge (TTB) system is a highly stochastic dynamical system. Deep learning has been applied to stochastic vibration analysis of TTB systems in recent years. However, most machine learning models consider only a single numerical relationship between input data and output responses. This often results in a strong dependence on training data, leading to a lack of robustness and reliability. In this paper, a novel hybrid method combining the probability density evolution method (PDEM) with an improved Bayesian optimization (BO) deep learning model (IDLM) is proposed for the efficient stochastic vibration analysis of uncertain TTB systems. This approach facilitates information exchange between the train-bridge model and the deep learning model. In this approach, PDEM is integrated into the deep learning framework to achieve a cohesive integration of physical and numerical models. The applicability of the PDEM-IDLM method is verified by comparing the predicted stochastic responses with the results of a validated train-bridge model. Furthermore, a case study investigates the effects of training dataset size, vehicle speed, and noise level, providing additional validation of the robustness of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110827"},"PeriodicalIF":9.4,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143219269","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 temporal-spatial multi-order weighted graph convolution network with refined feature topology graph for imbalance fault diagnosis of rotating machinery

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-14 DOI: 10.1016/j.ress.2025.110830

Zhichao Jiang , Dongdong Liu , Lingli Cui

{"title":"A temporal-spatial multi-order weighted graph convolution network with refined feature topology graph for imbalance fault diagnosis of rotating machinery","authors":"Zhichao Jiang , Dongdong Liu , Lingli Cui","doi":"10.1016/j.ress.2025.110830","DOIUrl":"10.1016/j.ress.2025.110830","url":null,"abstract":"<div><div>In the actual operation, rotating machinery mostly works under normal condition. The collected monitoring data often exhibit serious distribution imbalance with far more normal label samples than fault label samples, leading to poor recognition performance of standard intelligent diagnosis models. Besides, many intelligent diagnosis models rely on data generation to overcome this problem, which is subject to data generation differences. Therefore, to address above limitations, a novel temporal-spatial multi-order weighted graph convolution network (TSMOW-GCN) with refined feature topology graph is proposed. First, a multi-order weight graph convolution layer is proposed to aggregate multi-order weighted mixing neighbor information in different distances, which achieves broader representation and mines more features and relationships without data generation and deep network structure. Further, the feature modeling in temporal dimensions is considered. Second, a refined feature topology graph construction method is developed to obtain compact and efficient feature topology graphs, which can improve the ability of graph representation. Besides, a dynamically adjusted label smoothing regularization loss is proposed to further improve generalization ability and avoid overfitting of the trained model under imbalance data. Two rotating machinery datasets are used to quantitatively verify proposed method, indicating that the TSMOW-GCN outperforms several advanced approaches under various imbalance ratios.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110830"},"PeriodicalIF":9.4,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143219271","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

Analysis of system resilience in escalation scenarios involving LH2 bunkering operations

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-13 DOI: 10.1016/j.ress.2025.110816

Federica Tamburini, Matteo Iaiani, Valerio Cozzani

{"title":"Analysis of system resilience in escalation scenarios involving LH2 bunkering operations","authors":"Federica Tamburini, Matteo Iaiani, Valerio Cozzani","doi":"10.1016/j.ress.2025.110816","DOIUrl":"10.1016/j.ress.2025.110816","url":null,"abstract":"<div><div>In the context of global energy transition and decarbonization efforts, resilience emerges as a critical factor in ensuring the reliability and adaptability of industrial infrastructure systems. This paper introduces a novel model rooted in Dynamic Bayesian Networks (DBNs) for the quantitative assessment of the resilience of engineered systems in the event of escalation scenarios triggered by domino effect. The model is integrated into a systematic, step-by-step procedure capable of evaluating the ability of complex systems to recover functionality from subsequent disruptions occurring at different times throughout the operational lifecycle. Leveraging DBNs, the methodology captures the dynamic interactions and feedback among subsystems or components, overcoming the limitations associated with conventional methods. The innovative methodology has been applied to a case study involving a liquid hydrogen (LH<sub>2</sub>) bunkering system, illustrating its effectiveness in assessing resilience amidst evolving accident scenarios. The results demonstrate the significant impact of escalation scenarios on system resilience and underscore the importance of proper implementation and management of safety measures and mitigation strategies. The proposed approach provides a valuable insight into system performance and empowers proactive risk management in the face of escalation scenarios, ensuring the continued operation and success of industrial operations in an uncertain and interconnected reality.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110816"},"PeriodicalIF":9.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143219242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hydrogen impact on transmission pipeline risk: Probabilistic analysis of failure causes

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-12 DOI: 10.1016/j.ress.2025.110825

Ruochen Yang, Colin A. Schell, Dhruva Rayasam, Katrina M. Groth

{"title":"Hydrogen impact on transmission pipeline risk: Probabilistic analysis of failure causes","authors":"Ruochen Yang, Colin A. Schell, Dhruva Rayasam, Katrina M. Groth","doi":"10.1016/j.ress.2025.110825","DOIUrl":"10.1016/j.ress.2025.110825","url":null,"abstract":"<div><div>Transmission pipelines are the safest and most economical solution for long-distance hydrogen delivery. However, safety and reliability issues, such as hydrogen's impact on material properties including fracture toughness and fatigue crack growth, could restrict pipeline development. This impact may also increase the risk of several pipeline failure causes, including excavation damage, corrosion, earth movement, material failures, and other hydrogen damage mechanisms.</div><div>While many quantitative risk assessment (QRA) studies exist for natural gas pipelines, limited work focuses on hydrogen pipelines; the influence of hydrogen must be considered. This work presents a systematic causal model for hydrogen pipeline failures that incorporates multiple failure causes, quantifying hydrogen influence on pipeline failures and analyzing how changes in hydrogen effects or operating conditions impact multiple failure causes. According to the results, (1) hydrogen has a relatively minor impact on corrosion-related failure; (2) hydrogen greatly affects crack damage (the failure probability can increase by over 1000 times); (3) excavation damage is nearly independent of hydrogen's effects; (4) earth movement damage shows increased susceptibility (the failure probability can increase by over 10 times). The hydrogen effects change the relative susceptibility of pipelines to these failure causes, therefore, to implement tailored safety measures under varying operating conditions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110825"},"PeriodicalIF":9.4,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143352251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0