Reliability Engineering & System Safety最新文献_第4页

A data-driven framework for risk and resilience analysis in maritime transportation systems: A case study of domino effect accidents in arctic waters

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-19 DOI: 10.1016/j.ress.2025.111049

Shanshan Fu , Qinya Tang , Mingyang Zhang , Bing Han , Zhongdai Wu , Wengang Mao

{"title":"A data-driven framework for risk and resilience analysis in maritime transportation systems: A case study of domino effect accidents in arctic waters","authors":"Shanshan Fu , Qinya Tang , Mingyang Zhang , Bing Han , Zhongdai Wu , Wengang Mao","doi":"10.1016/j.ress.2025.111049","DOIUrl":"10.1016/j.ress.2025.111049","url":null,"abstract":"<div><div>Resilience is a complex concept that extends beyond risk, including the ability to absorb risks from external disturbances to maintain an acceptable level of safety. In the context of maritime transportation systems (MTS), resilience can be understood as a ship's ability to withstand disasters and ensure safe navigation in the face of unexpected incidents. This study proposes a data-driven framework for the quantitative analysis of risk and resilience in MTS, considering the temporal trends and domino effects of maritime accidents. The first step involves data preparation, which includes the collection, processing, and storage of global maritime accident data from the Lloyd's List Intelligence database spanning from 2014 to 2023. Next, an analysis of evolution trends is conducted to explore temporal trends and domino effects, focusing on the severity and pollution of maritime accidents. Arctic waters, known for their typical domino effects in maritime accidents, are chosen as a case study to illustrate the proposed risk and resilience analysis approach by considering the absorptive capacity in the evolution of maritime accidents. Furthermore, proactive and reactive risk control options are suggested for critical domino accident scenarios in Arctic waters to provide targeted recommendations for managing risks in Arctic shipping.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111049"},"PeriodicalIF":9.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683431","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 novel active learning stochastic Kriging metamodel for improving reliability and stability of additive manufacturing processes

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-19 DOI: 10.1016/j.ress.2025.111043

Chunfeng Ding , Jianjun Wang , Suying Zhang , Shijuan Yang , Yizhong Ma

{"title":"A novel active learning stochastic Kriging metamodel for improving reliability and stability of additive manufacturing processes","authors":"Chunfeng Ding , Jianjun Wang , Suying Zhang , Shijuan Yang , Yizhong Ma","doi":"10.1016/j.ress.2025.111043","DOIUrl":"10.1016/j.ress.2025.111043","url":null,"abstract":"<div><div>Instability in the manufacturing process may lead to significant differences in product quality characteristics under the same set of process parameters, thus directly affecting the reliability and consistency of the product. Reducing quality variation by optimizing process parameters is the key to improving process stability. Process instability results in quality characteristic data with a low signal-to-noise ratio, thereby affecting the optimization and control of process parameters. Furthermore, high manufacturing costs also restrict the sample size available for training models. To identify more robust process parameters at a lower cost and ensure product quality consistency, this study proposes a novel active learning method based on the stochastic Kriging model. Firstly, we considered the necessity of replication and the heterogeneity of variance, establishing a heteroscedastic Gaussian process model that allows for fast inference. Secondly, we proposed an integrated mean squared prediction error (IMSPE) optimization strategy based on an active learning method to balance replication and exploration with fewer samples. Finally, numerical examples demonstrated the effectiveness and advantages of the proposed modeling and active learning methods. A 3D printing case further confirmed that the proposed method outperforms other competing methods regarding model prediction performance and the robustness of optimal process parameters.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111043"},"PeriodicalIF":9.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683422","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

Asymptotic subset simulation: An efficient extrapolation tool for small probabilities approximation

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-18 DOI: 10.1016/j.ress.2025.111034

Mohsen Rashki , Matthias G.R. Faes , Pengfei Wei , Jingwen Song

{"title":"Asymptotic subset simulation: An efficient extrapolation tool for small probabilities approximation","authors":"Mohsen Rashki , Matthias G.R. Faes , Pengfei Wei , Jingwen Song","doi":"10.1016/j.ress.2025.111034","DOIUrl":"10.1016/j.ress.2025.111034","url":null,"abstract":"<div><div>This study bridges the concepts of subset simulation with asymptotic approximation theory in multinormal integrals for the estimation of small probabilities. To meet this aim, for a sequence of scaled limit state functions (LSFs) with failure probabilities higher than the original LSF, it is found that the proposed asymptotic approximation and subset simulation can be applied within the same framework, and only a few steps of subset simulation could be sufficient to approximate small failure probabilities using extrapolation. The analogy of the formulation of the second-order reliability method (SORM) with the proposed concept is studied and, considering sequential sampling as a search algorithm, shown that the information obtained from a few steps of the design point search process could be enough to approximate the total failure probability of a problem. Solving intricate nonlinear and high-dimensional problems confirms the efficiency and robustness of the proposed framework for reliability analysis of real-world engineering problems with small probabilities.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111034"},"PeriodicalIF":9.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704006","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 novel computational framework for efficient nuclear containment design: Structural integrity, radiation shielding, and reliability assessment

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-17 DOI: 10.1016/j.ress.2025.111038

Sanchit Saxena , Suman Kumar , Hrishikesh Sharma

{"title":"A novel computational framework for efficient nuclear containment design: Structural integrity, radiation shielding, and reliability assessment","authors":"Sanchit Saxena , Suman Kumar , Hrishikesh Sharma","doi":"10.1016/j.ress.2025.111038","DOIUrl":"10.1016/j.ress.2025.111038","url":null,"abstract":"<div><div>This research introduces a novel, multidisciplinary framework for the design of nuclear containment structures (NCS), integrating radiation shielding, structural integrity, and reliability assessment. Unlike conventional approaches, this study uniquely incorporates moment-curvature (M-ϴ) analysis for structural evaluation, Monte Carlo (MC) simulations for radiation shielding performance, and reliability analysis to ensure robust and efficient design. Radiation shielding studies showed that primary particles' effective dose rate (EDR) varies significantly with rebar configurations, while secondary particle EDR remains constant, decreasing only with increased section thickness. A novel SD factor was introduced to quantify the impact of rebar spacing and size on primary EDR values, providing a new metric for optimizing shielding efficiency. M-ϴ analysis identified 37 configurations with equivalent yield moments for targeted strength levels. Optimal rebar configurations for each section thickness were identified, with changes in rebar configurations leading to up to 56.85 % variation in EDR, emphasizing the role of rebar in enhancing radiation shielding. Reliability analysis identified a 1.3 m section with 44 mm rebar at 195 mm spacing as ideal, ensuring 99.74 % (β=2.7995) reliability in maintaining safe EDR levels. These findings provide essential insights for standardized NCS guidelines that prioritize strength and radiation shielding for safer, efficient designs.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111038"},"PeriodicalIF":9.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143703897","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

Causation analysis of ship collisions using a TM-FRAM model

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-17 DOI: 10.1016/j.ress.2025.111035

Yuhong Wang , Pengchang Li , Cheng Hong , Zaili Yang

{"title":"Causation analysis of ship collisions using a TM-FRAM model","authors":"Yuhong Wang , Pengchang Li , Cheng Hong , Zaili Yang","doi":"10.1016/j.ress.2025.111035","DOIUrl":"10.1016/j.ress.2025.111035","url":null,"abstract":"<div><div>Ship collisions pose a significant threat to life and property, presenting a major challenge in maritime safety. Current risk analysis methods have been criticized in terms of a lack of capacity of quantifying the risks of different features and a standardized database reflecting the multidimensional risks of human, mechanical, environmental, and management factors. Additionally, traditional analysis sometimes involves strong assumptions that 1) the established and widely used databases can capture all the essential features of ship collisions and 2) the modelling of ship collision process can be simplified by focusing the analysis on a single causal relationship at once. This paper aims to develop a new approach to enabling multi-dimensional analysis of the causation of ship collisions through the establishment of a new database for ship collisions by innovatively combining Text Mining (TM), Association Rule (AR), and Functional Resonance Analysis Method (FRAM). The new approach enables to construct a risk analysis network based on FRAM, and the model's practicality and effectiveness are validated through expert reviews and case studies. As a result, thirty-eight key risk factors have been successfully identified as per their influence to ship collision incidents, encompassing human error, mechanical failures, adverse environmental conditions, and operational issues. The findings not only offer a new perspective and methodology for ship collision risk analysis, but also enrich the theoretical framework of ship safety management, providing valuable guidance for ensuring ship navigation safety.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111035"},"PeriodicalIF":9.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683434","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

Image texture feature fusion enhancement for bearing fault diagnosis based on maximum gradient

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-17 DOI: 10.1016/j.ress.2025.111009

Yongjian Sun, Gang Yu, Wei Wang

{"title":"Image texture feature fusion enhancement for bearing fault diagnosis based on maximum gradient","authors":"Yongjian Sun, Gang Yu, Wei Wang","doi":"10.1016/j.ress.2025.111009","DOIUrl":"10.1016/j.ress.2025.111009","url":null,"abstract":"<div><div>In modern manufacturing industry, mechanical equipment plays a crucial role. In order to address the difficulty of signal feature extraction in mechanical equipment, this paper proposes a image Texture Feature Fusion Enhancement (TFFE) method based on maximum gradient. A mathematical transformation method is used to convert one-dimensional time series into two forms of images: symmetrized dot pattern and scalogram. The texture features are obtained by calculating the maximum gradient of the two types of images. The proposed image Texture Feature Fusion Enhancement (TFFE) method is used to combine different images and enhance the texture features. Finally, the Darknet53 network is used as the image classification method to conduct intelligent classification of rolling bearing faults. The classification effect of the method is verified by a series of experiments, in which the validity of the images used in different image conditions is verified, and the network used in different network conditions show better classification performance. The method’s ability to resist noise is also validated in experiments under different noise conditions. The experimental results show that the proposed image enhancement method can improve fault features in the image and maintain good diagnostic performance.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111009"},"PeriodicalIF":9.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642762","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

Integrated maintenance service and spare parts supply strategy for service-oriented manufacturers with partially unknown information

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-17 DOI: 10.1016/j.ress.2025.111030

Ying Zhu, Tangbin Xia, Shuo Gao, Ge Hong, Ershun Pan, Lifeng Xi

{"title":"Integrated maintenance service and spare parts supply strategy for service-oriented manufacturers with partially unknown information","authors":"Ying Zhu, Tangbin Xia, Shuo Gao, Ge Hong, Ershun Pan, Lifeng Xi","doi":"10.1016/j.ress.2025.111030","DOIUrl":"10.1016/j.ress.2025.111030","url":null,"abstract":"<div><div>Original equipment manufacturers (OEMs) are increasingly penetrating the aftersales market by providing maintenance services and OEM parts. Competition from independent maintenance service providers and independent parts suppliers necessitates that OEMs optimize strategies to enhance competitiveness while maintaining cost efficiency. Different from the independent operation of either business, the integration of OEMs’ dual roles creates significant interactions between maintenance service and spare parts supply. Particularly, within the constraints of production and inventory capacities, dynamic spare parts demand from internal maintenance needs and external orders complicates production quantity decisions and inventory allocation. From the perspective of service-oriented manufacturers under dual competition, this paper proposes an optimization model to maximize the expected total profits from maintenance services and spare parts sales. The model captures the interactions among decisions including pricing for service and spare parts, maintenance intervals, production quantities, and inventory allocation for dual-source demand. However, the differing decision timelines and partially unknown market information pose challenges to the collaborative optimization of these strategies. To address this, a two-stage algorithm integrating reinforcement learning is proposed, providing a “predetermination and online adjustment” mechanism. Numerical studies validate the advantages of the proposed methodology in mitigating potential profit loss due to independent optimization and estimation errors.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111030"},"PeriodicalIF":9.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683423","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

Availability analysis of phase mission weighted components system with common bus performance sharing

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-16 DOI: 10.1016/j.ress.2025.111024

Vandana Bhatt, S.B. Singh

引用次数: 0

Resilience modeling and evaluation of multi-state system with common bus performance sharing under dynamic reconfiguration

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-16 DOI: 10.1016/j.ress.2025.111040

Gengshuo Hu, Xing Pan, Jian Jiao

{"title":"Resilience modeling and evaluation of multi-state system with common bus performance sharing under dynamic reconfiguration","authors":"Gengshuo Hu, Xing Pan, Jian Jiao","doi":"10.1016/j.ress.2025.111040","DOIUrl":"10.1016/j.ress.2025.111040","url":null,"abstract":"<div><div>Multi-state systems with common bus performance sharing (MSS-CBPS) are widely applied in the industry, including integrated modular avionics (IMA) and computing systems. In the presence of uncertain disturbances, dynamic reconfiguration can enhance the ability to manage these uncertainties effectively. Resilience can significantly describe the ability of systems to recover from disturbances. However, effective methods have not fully been proposed to model and evaluate their resilience against disturbances. To address this issue, this paper introduces a novel resilience model based on the structure and characteristics of the MSS-CBPS, incorporating dynamic reconfiguration strategies and performance allocation sequences. Furthermore, the model comprehensively evaluates system resilience through resistance, response, and recovery resilience metrics. Based on the novel resilience model, an algorithm based on the universal generating function (UGF) is developed to evaluate system resilience accurately under different dynamic reconfiguration strategies. Finally, the model and algorithm are applied to an integrated task processing system in a helicopter to demonstrate their feasibility by analyzing resilience tendencies under different dynamic reconfiguration strategies. The results also provide valuable insights for designing integrated task processing systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111040"},"PeriodicalIF":9.4,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683517","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

Assist in real-time risk evaluation induced by electrical cabinet fires in nuclear power plants: A dual AI framework employing BiTCN and TCNN

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-15 DOI: 10.1016/j.ress.2025.111037

Qiuju Ma , Zhennan Chen , Jianhua Chen , Yubo Sun , Nan Chen , Mengzhen Du

{"title":"Assist in real-time risk evaluation induced by electrical cabinet fires in nuclear power plants: A dual AI framework employing BiTCN and TCNN","authors":"Qiuju Ma , Zhennan Chen , Jianhua Chen , Yubo Sun , Nan Chen , Mengzhen Du","doi":"10.1016/j.ress.2025.111037","DOIUrl":"10.1016/j.ress.2025.111037","url":null,"abstract":"<div><div>Electrical cabinet fires in nuclear power plants pose significant threats to reactor safety. While numerous studies have investigated cabinet fires, risk real-time evolution induced by high-temperature smoke layer has not received sufficient attention. Consequently, this study proposes a dual AI framework, integrating the Bidirectional Temporal Convolutional Network (BiTCN) and Transposed Convolutional Neural Network (TCNN), to predict temperature field in advance. Database is constructed by Fire Dynamics Simulator (FDS), featuring various burner heights, heat release rates, and ventilation conditions. Temperature beneath ceiling and temperature field are recorded. Thermocouple data is used to train BiTCN for forecasting ceiling temperature with a lead time of 60 s. The trained BiTCN model achieved an exceptional R<sup>2</sup> value exceeding 0.999. Compared to other methods, BiTCN has advantages in accuracy and computational efficiency. The TCNN takes output from BiTCN as input and FDS temperature slice results as output labels to deduce real-time changes in two-dimensional temperature field. It achieves an R<sup>2</sup> value of 0.973. Although some discrepancies exist, results indicate strong predictive capability and reliability in capturing spatial and temporal dynamics of temperature field. This work demonstrates potential of using Artificial Intelligence (AI) to predict dynamic evolution of cabinet fires and represents a significant exploration of applying AI in nuclear safety.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111037"},"PeriodicalIF":9.4,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683513","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