Reliability Engineering & System Safety最新文献_第9页

Digital twin-enhanced opportunistic maintenance of smart microgrids based on the risk importance measure 基于风险重要性度量的数字孪生增强型智能微电网机会维护

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI: 10.1016/j.ress.2024.110548

引用次数: 0

Resilience assessment of FPSO leakage emergency response based on quantitative FRAM 基于定量 FRAM 的 FPSO 泄漏应急响应复原力评估

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI: 10.1016/j.ress.2024.110526

{"title":"Resilience assessment of FPSO leakage emergency response based on quantitative FRAM","authors":"","doi":"10.1016/j.ress.2024.110526","DOIUrl":"10.1016/j.ress.2024.110526","url":null,"abstract":"<div><div>FPSO production process is prone to leakage, and failure to respond promptly and effectively will lead to accident escalation and serious consequences. However, traditional safety assessment methods cannot handle the nonlinear relationships between human, technology, and organization in emergency response process. This study proposes a quantitative FRAM to evaluate the emergency response resilience of FPSO leakage. This method establishes a resilience evaluation framework including three tiers: function, ability, and system, which can quantify system resilience based on the variability of function. First, identify basic functions according to the four stages of monitoring, response, learning and anticipation in the emergency response process, and establish the FRAM model of FPSO leakage emergency response. Then, the quantitative FRAM and Monte Carlo simulation are combined to calculate the variabilities of functions under different operating conditions. Finally, based on the simulation results, the variabilities of basic functions are aggregated and statistically analyzed to quantify system resilience. The implementation process of this method is illustrated by a case study. The influence of different factors on the system resilience is analyzed by setting various operation scenarios, and critical functions are identified by sensitivity analysis, which can provide reference for improving system resilience and ensuring FPSO safety production.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421441","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

Rapid computation of survival signature for dynamic fault tree based on sequential binary decision diagram and multidimensional array 基于顺序二元决策图和多维阵列的动态故障树生存特征快速计算

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI: 10.1016/j.ress.2024.110552

{"title":"Rapid computation of survival signature for dynamic fault tree based on sequential binary decision diagram and multidimensional array","authors":"","doi":"10.1016/j.ress.2024.110552","DOIUrl":"10.1016/j.ress.2024.110552","url":null,"abstract":"<div><div>Many practical safety-critical systems typically exhibit sequence-dependent failure behaviors, limiting the efficiency of analyzing these systems. Although the survival signature-based method can address this problem to a certain extent, the dependence on Boolean states constrains its application to large systems. In this study, we present a novel method that leverages the sequential binary decision diagram (SBDD) and multidimensional array to rapidly compute survival signatures for dynamic fault trees (DFTs) of these systems. These dynamic nodes in the SBDD are represented through multidimensional arrays, which are then utilized as inputs for the subsequent computations. Ultimately, survival signatures are obtained by iteratively computing the multidimensional arrays. Additionally, two practical engineering cases are examined to highlight the superiority of the proposed methods over other methods. Compared with Boolean state vector-based methods, the proposed method achieves a 689-fold and 209-fold increase in efficiency for calculating survival signatures in their respective cases. Compared with the Monte Carlo (MC) simulation, the simulation efficiency for the reliability results improve by 60-fold and 201-fold in their respective cases.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421435","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 comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier 在无时间标识符的有限数据条件下估算锂离子电池剩余使用寿命的综合框架

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI: 10.1016/j.ress.2024.110517

{"title":"A comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier","authors":"","doi":"10.1016/j.ress.2024.110517","DOIUrl":"10.1016/j.ress.2024.110517","url":null,"abstract":"<div><div>The escalating applications of Lithium-ion (Li-ion) batteries in renewable energy and electric vehicles underscore the need for enhanced prognostics and health management systems to reduce the risk of sudden failures. Remaining useful life (RUL) determination is one of the most critical tasks in the field of battery prognostics nowadays. Even though statistical and machine learning (ML) methods have proven effective in research setups, many challenges prevent applying these prediction methods to real-life scenarios. These challenges include (1) scarcity of run-to-failure datasets with similar experimental conditions, (2) low data granularity when presented in capacity vs. discharge cycle pairs, and (3) lack of “temporal identifiers” in real-life scenarios. A temporal identifier is any label that provides knowledge about the current degradation state of a working battery. The research question developed for this study was, ‘Can the remaining useful life of a Li-ion battery having limited data without a temporal identifier be predicted?’ The specific aims were to estimate the temporal identifier of limited data and to predict the remaining useful life (RUL). An innovative framework incorporating reliability analysis and deep learning addresses these specific aims. Experimental data is used to test the framework's capabilities, limiting the training dataset to only three batteries and the testing dataset to a small sample (< 10 data points) of another battery. This new approach enabled the RUL prediction to achieve errors as low as ∼5 cycles and root mean square error of 6.24 cycles, outperforming other benchmark studies on Li-ion battery RUL prediction that use more battery degradation data without temporal identifier.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420991","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

Time-evolving traffic resilience performance forecasting during hazardous weather toward proactive intervention 在危险天气期间进行时变交通恢复性能预测，实现主动干预

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI: 10.1016/j.ress.2024.110521

{"title":"Time-evolving traffic resilience performance forecasting during hazardous weather toward proactive intervention","authors":"","doi":"10.1016/j.ress.2024.110521","DOIUrl":"10.1016/j.ress.2024.110521","url":null,"abstract":"<div><div>Transportation systems experience significant disruptions and loss during hazardous weather events, exhibiting great needs of timely intervention to effectively improve the resilience of the affected traffic systems. An informed and science-based proactive intervention strategy depends on accurate forecasting of the resilience performance of traffic systems with essential lead time during hazards. A new resilience performance forecasting methodology at both global and local scales is proposed for traffic networks under natural hazards by addressing unique challenges such as scarcity and time-evolving nature of hazard-specific data. The proposed methodology consists of two modules: the local traffic resilience performance short-term forecasting module based on the modified diffusion convolutional recurrent neural network (DCRNN) and transfer learning techniques, and the global traffic resilience performance forecasting module integrating percolation-based robustness assessment and SIR-based congestion propagation modeling. A case study of an urban traffic network during a major snowstorm hazard is conducted as a demonstration, followed by the feasibility investigation to guide proactive intervention during hazards. It is found the proposed methodology can forecast the time-evolving traffic resilience performance with good accuracy at both global and local scales. With sufficient lead time for the forecast, it bears promising potential to assist the stakeholders to make informed and timely decision about possible proactive intervention by providing key information to help identify the optimal moments and individual strategic links for possible intervention.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421442","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 Monte Carlo-based modeling method for the spatial-temporal evolution process of multi-hazard and higher-order domino effect 基于蒙特卡洛的多灾害和高阶多米诺效应时空演化过程建模方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-04 DOI: 10.1016/j.ress.2024.110532

{"title":"A Monte Carlo-based modeling method for the spatial-temporal evolution process of multi-hazard and higher-order domino effect","authors":"","doi":"10.1016/j.ress.2024.110532","DOIUrl":"10.1016/j.ress.2024.110532","url":null,"abstract":"<div><div>The domino effect in chemical industrial parks represents a complex phenomenon where accidents such as leaks, fires, and explosions can occur either simultaneously or in sequence. The progression of domino accidents is highly uncertain, making it difficult to anticipate the spatial-temporal development of such accidents. This paper presents a model that aims to forecast the evolution of domino effects by considering the critical thermal dose and utilizing the Probit model to assess the escalation of incidents caused by thermal radiation and overpressure. To tackle the complexities associated with multiple installations, high order, and various accident types in modeling domino effect accidents, the model incorporates Monte Carlo simulation methods. The model validation and case studies have demonstrated the effectiveness of this approach in simulating the progression of domino accidents initiated by a range of primary accidents. This approach enables the prediction of potential accident chains and the dynamic failure probability of hazardous installations, including the identification of the initial installation likely to fail. The insights gained from this research offer guidance for the prevention and mitigation of the domino effect in chemical accidents.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442947","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 dynamic importance function for accidental scenarios generation by RESTART in the computational risk assessment of cyber-physical infrastructures 在网络物理基础设施的计算风险评估中使用 RESTART 生成意外情况的动态重要性函数

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-04 DOI: 10.1016/j.ress.2024.110538

{"title":"A dynamic importance function for accidental scenarios generation by RESTART in the computational risk assessment of cyber-physical infrastructures","authors":"","doi":"10.1016/j.ress.2024.110538","DOIUrl":"10.1016/j.ress.2024.110538","url":null,"abstract":"<div><div>The Computational Risk Assessment (CRA) of Cyber-Physical Systems (CPSs) calls for the analysis of accidental scenarios emerging from the complexities and interdependencies typical of CPSs. Generating these scenarios via crude Monte Carlo Simulation (MCS) is impractical due to the high computational demand of simulation codes of CPSs, considering the combinatorial number of possible scenarios. In this paper, we tailor the use of Repetitive Simulation Trials After Reaching Thresholds (RESTART), a rare-event simulation method of literature, to efficiently generate relevant accidental scenarios. The tailored RESTART is guided by a dynamic Importance Function (IF) originally introduced here to dynamically characterize the relevance of the scenarios with reference to the current topology of the CPS and the susceptibility of its components. Two case studies of increasing complexity are considered: a single power grid and a CPS consisting of an Integrated Power and Telecommunication (IP&TLC) infrastructure. Results show that RESTART mines out more relevant scenarios than crude MCS for a number of different IFs based on vulnerability metrics of literature, and thus particularly efficiently when the novel IF is adopted.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446049","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

An agent-based framework for resilience analysis of service networks 基于代理的服务网络复原力分析框架

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-02 DOI: 10.1016/j.ress.2024.110523

{"title":"An agent-based framework for resilience analysis of service networks","authors":"","doi":"10.1016/j.ress.2024.110523","DOIUrl":"10.1016/j.ress.2024.110523","url":null,"abstract":"<div><div>Service networks made up of nodes and links inevitably suffer from performance degradation due to the negative effect of natural disasters and intentional attacks. Resilience is defined as the capability of recovering from disruptive events. Resilience analysis is of vital importance to evaluate network performance during the whole operation process. Given the dynamic characteristic of service networks, it is difficult to reflect the actual performance through static models. Moreover, resilience assessment should proceed with multiple metrics because network resilience is a multifaceted capability. To this end, an agent-based framework for resilience analysis is developed in this paper. Nodes are regarded as agents with independent decision-making to better respond to disruptions. The multi-agent negotiation mechanism is introduced to satisfy service requirements using deep Q-learning. In addition, network resilience is comprehensively assessed in terms of reliability, supportability and maintainability. A case study of Iridium system is conducted to illustrate the applicability of the agent-based framework. The results show that the developed framework can select the optimal route for task assignment and quantify resilience in the dynamic environment.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421439","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

An energy IoT-driven multi-dimension resilience methodology of smart microgrids 能源物联网驱动的智能微电网多维弹性方法论

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-02 DOI: 10.1016/j.ress.2024.110533

{"title":"An energy IoT-driven multi-dimension resilience methodology of smart microgrids","authors":"","doi":"10.1016/j.ress.2024.110533","DOIUrl":"10.1016/j.ress.2024.110533","url":null,"abstract":"<div><div>Smart microgrids are significant in promoting clean energy development and improving microgrid security and reliability. However, harsh environments make them exposed to various hazards, including natural hazards such as hail and wildfire and digital hazards such as cyberattacks. Due to these complex challenges, performing performance evaluation and resilience analysis for smart microgrids in different periods (e.g., before, during, and after the hazards) and different layers (e.g., a data layer and a physical layer) is difficult. To reduce this research gap, this paper develops a new multi-layer failure and multi-dimension resilience methodology in the energy Internet of Things (IoT). This paper analyses a multi-layer failure mechanism of smart microgrids in energy IoT with the synergy of the “physical layer, perception layer, communication layer, and application layer”, establishes a multi-stage performance model for smart microgrids based on operation loops, and develops a multi-dimension resilience methodology for smart microgrids with consideration of four performance evolution processes (i.e., prevention, degradation, restoration, and reconstitution). A case adopted from the Shandong province in China is used to demonstrate the proposed method under normal operating conditions and different types of disasters.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420993","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

Assessment on resilience of urban agglomeration transportation system considering passenger choice and load-capacity factor 考虑乘客选择和运载能力因素的城市群交通系统复原力评估

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-02 DOI: 10.1016/j.ress.2024.110527

{"title":"Assessment on resilience of urban agglomeration transportation system considering passenger choice and load-capacity factor","authors":"","doi":"10.1016/j.ress.2024.110527","DOIUrl":"10.1016/j.ress.2024.110527","url":null,"abstract":"<div><div>Intercity transportation system (ICTS), characterized by large-scale, high spatial-temporal concentration, and sparser departure frequencies, is more vulnerable in unexpected events. Understanding the resilience characteristics of ICTS is crucial for maintaining the network service capabilities. Aiming to conduct effective resilience assessment on ICTS, we develop the resilience simulation model by introduce dual-regulated parameters for network load and capacity into cascading propagation model under interruption events, and quantify the impact of travel distance, time costs, and route redundancy on travel choice of passengers. Meanwhile, propose service resilience indicators from both the passenger's and the system's perspectives. Finally, we conduct a case study on the resilience of ICTS in Beijing-Tianjin-Hebei Urban Agglomerations (BTH-UA). The results show that: 1) Multimodal transportation systems usually exhibit better resilience than unimodal systems. 2) For various resilience optimization metrics, it is essential to choose targeted recovery strategies to maximize network resilience. 3) Traveler sensitivity to travel time significantly influences the resilience of passenger-based network services. 4) Changes in transportation supply capacity and travel demand will impact the system's resilience. The research findings can provide valuable references for the resilience development and management of urban transportation systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421568","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