Reliability Engineering & System Safety最新文献_第10页

Quantitative risk assessment for connected automated Vehicles: Integrating improved STPA-SafeSec and Bayesian network 联网自动驾驶汽车的定量风险评估：整合改进的 STPA-SafeSec 和贝叶斯网络

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI: 10.1016/j.ress.2024.110528

Qi Liu , Ke Sun , Wenqi Liu , Yufeng Li , Xiangyu Zheng , Chenhong Cao , Jiangtao Li , Wutao Qin

{"title":"Quantitative risk assessment for connected automated Vehicles: Integrating improved STPA-SafeSec and Bayesian network","authors":"Qi Liu , Ke Sun , Wenqi Liu , Yufeng Li , Xiangyu Zheng , Chenhong Cao , Jiangtao Li , Wutao Qin","doi":"10.1016/j.ress.2024.110528","DOIUrl":"10.1016/j.ress.2024.110528","url":null,"abstract":"<div><div>Connected automated vehicles (CAVs) risk assessment is of paramount significance, as it integrates safety and security factors to ensure dependable operation while effectively mitigating potential hazards and vulnerabilities. However, existing risk assessment methods suffer from two shortcomings: shying away from quantification and insufficiently considering threats. To this end, we propose a quantifiable risk assessment method, which incorporates the STRIDE threat model to address cybersecurity concerns within the context of CAVs. Specifically, we first present improved STPA-SafeSec for hazard analysis, using a generic causal factor diagram and STRIDE to identify causal factors, safety and security requirements, and the corresponding mitigations. Then, we propose a Bayesian Network for comprehensive quantification of system risk. This approach enables quantitative risk assessment, sensitivity analysis, prioritization of risk control measures, and benefit cost analysis that aided by a designed greedy optimization algorithm. A case study on a real open-source test vehicle demonstrates that the proposed method not only offers a comprehensive analysis of hazards and vulnerabilities, but also provides a quantitative risk assessment. Comparative assessments suggest that the proposed method exhibits a notable advantage in terms of analysis results (utility), analysis steps (usability), and the analysis process (efficiency) when compared to existing approaches.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110528"},"PeriodicalIF":9.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420994","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

Probability density estimation of polynomial chaos and its application in structural reliability analysis 多项式混沌的概率密度估计及其在结构可靠性分析中的应用

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI: 10.1016/j.ress.2024.110537

Ye-Yao Weng, Teng Liu, Xuan-Yi Zhang, Yan-Gang Zhao

{"title":"Probability density estimation of polynomial chaos and its application in structural reliability analysis","authors":"Ye-Yao Weng, Teng Liu, Xuan-Yi Zhang, Yan-Gang Zhao","doi":"10.1016/j.ress.2024.110537","DOIUrl":"10.1016/j.ress.2024.110537","url":null,"abstract":"<div><div>Polynomial chaos expansion (PCE) is a widely used approach for establishing the surrogate model of a time-consuming performance function for the convenience of uncertainty quantification of a stochastic structure. However, it remains difficult to calculate the probability density function (PDF) of the PCE accurately for general cases, though the PDF, as a complete representation of a random variable, is often required in some uncertainty problems. To address this problem, this paper proposes a semi-analytical method to compute the PDF of a PCE. This method derives the closed-form solutions of characteristic functions (CFs) of the first- and second-order PCEs, while an equivalent parabolization technique is proposed to provide the approximate solutions of CFs of higher-order PCEs. Then, the PDF of the PCE can be obtained by the Fourier transform of the resulting CF. Three numerical examples are investigated to demonstrate the accuracy, applicability, and efficiency of the proposed method for probability density estimation of PCE in structural reliability analysis.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110537"},"PeriodicalIF":9.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446046","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

Domain correction for hydraulic internal pump leakage detection considering multiclass aberrant flow data 考虑多类畸变流量数据的液压内泵泄漏检测领域校正

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI: 10.1016/j.ress.2024.110539

Xirui Chen, Hui Liu

引用次数: 0

Contrastive domain-invariant generalization for remaining useful life prediction under diverse conditions and fault modes 在不同条件和故障模式下预测剩余使用寿命的对比域不变广义方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI: 10.1016/j.ress.2024.110534

Xiaoqi Xiao , Jianguo Zhang , Dan Xu

{"title":"Contrastive domain-invariant generalization for remaining useful life prediction under diverse conditions and fault modes","authors":"Xiaoqi Xiao , Jianguo Zhang , Dan Xu","doi":"10.1016/j.ress.2024.110534","DOIUrl":"10.1016/j.ress.2024.110534","url":null,"abstract":"<div><div>As industrial equipment becomes increasingly complex, necessitating operation under varied conditions and often exhibiting diverse failure modes, traditional deep learning models built on data from the original environment become inapplicable. Moreover, in actual industrial scenarios, the generalization capability of Domain Adaptation and classic Domain Generalization methods is severely impacted when there is a lack of multiple source domain and target domain data, due to the cost or feasibility constraints associated with collecting extensive monitoring data. In this paper, a single domain Contrastive Domain-Invariant Generalization (CDIG) method for estimating the remaining useful life under different conditions and fault modes is proposed. This method first defines homologous signals as the foundational data. Subsequently, it learns domain-invariant features by encouraging two feature extraction processes to extract latent features of homologous signals as similarly as possible. Additionally, multiple condition-based attention, pooling, and a novel equalization loss function are utilized to regulate the generation of domain-invariant features. Ultimately, the RUL predictor is trained by source domain data, operational conditions, and temporal information to facilitate its applicability across diverse domains. Case studies demonstrate that CDIG achieves satisfactory predictive results under unseen conditions, highlighting the potential of the proposed method as an effective predictive tool.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110534"},"PeriodicalIF":9.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420990","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 multi-stage stochastic programming model for multi-mission selective maintenance optimization 多任务选择性维护优化的多阶段随机编程模型

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI: 10.1016/j.ress.2024.110551

Milad Ghorbani , Mustapha Nourelfath , Michel Gendreau

引用次数: 0

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

Hongyan Dui , Songru Zhang , Xinghui Dong , Shaomin Wu

引用次数: 0

Adaptive support vector machine for time-variant failure probability function estimation 用于估算时变故障概率函数的自适应支持向量机

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI: 10.1016/j.ress.2024.110510

Weiming Zheng, Xiukai Yuan, Xiya Bao, Yiwei Dong

{"title":"Adaptive support vector machine for time-variant failure probability function estimation","authors":"Weiming Zheng, Xiukai Yuan, Xiya Bao, Yiwei Dong","doi":"10.1016/j.ress.2024.110510","DOIUrl":"10.1016/j.ress.2024.110510","url":null,"abstract":"<div><div>Time variant reliability analysis introduces additional complexity due to the inclusion of time. When the time-variant failure probability function (TFPF) of the structure is of interest, it inherently involves sequential evaluations of the failure probabilities of series systems varied with time in discretized space, posing a challenge to reliability analysis. An efficient approach for the evaluation of the TFPF, called ‘Time-dependent Adaptive Support Vector Machine combined with Monte Carlo Simulation’ (TASVM-MCS), is presented to reduce the corresponding computational cost. Based on the samples from Monte Carlo simulation (MCS), an iterative strategy is proposed to actively extract the most valuable sample points from the sample pool and iteratively update the support vector machine (SVM) model. In particular, an active learning function is proposed to take into account the diversity of samples and time simultaneously. In this way, the built SVM will be more suitable for the evaluation of TFPF other than a point-wise failure probability. The proposed TASVM-MCS method is relatively less sensitive to the dimensionality of the input variables, making it a powerful and promising approach for time-variant reliability computations. Four representative examples are given to demonstrate the significant effectiveness and efficiency of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110510"},"PeriodicalIF":9.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421431","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

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

Jianxing Yu , Qingze Zeng , Yang Yu , Baolei Zhang , Wentao Ma , Shibo Wu , Hongyu Ding , Zhenmian Li

{"title":"Resilience assessment of FPSO leakage emergency response based on quantitative FRAM","authors":"Jianxing Yu , Qingze Zeng , Yang Yu , Baolei Zhang , Wentao Ma , Shibo Wu , Hongyu Ding , Zhenmian Li","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":"253 ","pages":"Article 110526"},"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

Shaoxuan Wang , Daochuan Ge , Nuo Yong , Ming Sun , Yuantao Yao , Longlong Tao , Dongqin Xia , Feipeng Wang , Jie Yu

{"title":"Rapid computation of survival signature for dynamic fault tree based on sequential binary decision diagram and multidimensional array","authors":"Shaoxuan Wang , Daochuan Ge , Nuo Yong , Ming Sun , Yuantao Yao , Longlong Tao , Dongqin Xia , Feipeng Wang , Jie Yu","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":"253 ","pages":"Article 110552"},"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

Camilo Lopez-Salazar , Stephen Ekwaro-Osire , Shweta Dabetwar , Fisseha Alemayehu

{"title":"A comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier","authors":"Camilo Lopez-Salazar , Stephen Ekwaro-Osire , Shweta Dabetwar , Fisseha Alemayehu","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":"253 ","pages":"Article 110517"},"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