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

An ontology-based multi-hazard coupling accidents simulation and deduction system for underground utility tunnel - A case study of earthquake-induced disaster chain 基于本体的地下管线隧道多灾害耦合事故模拟与推演系统--地震诱发灾害链案例研究

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-06 DOI: 10.1016/j.ress.2024.110559

{"title":"An ontology-based multi-hazard coupling accidents simulation and deduction system for underground utility tunnel - A case study of earthquake-induced disaster chain","authors":"","doi":"10.1016/j.ress.2024.110559","DOIUrl":"10.1016/j.ress.2024.110559","url":null,"abstract":"<div><div>Integrated underground utility tunnels are increasingly crucial in modern cities, addressing the pressing need for sustainable urban development. However, their extensive centralization amplifies both the complexity and scale of potential risks. When a utility tunnel accident occurs, it is possible to trigger a sequence of cascading events, thereby resulting a complex coupling accident. While previous research has predominantly focused on individual hazards, understanding multi-hazard coupling accidents presents significant challenges and lacks effective methodologies. In this paper, we propose an integrated system utilizing ontology technology and knowledge base construction for simulating and deducing coupling accidents in urban utility tunnels. Specifically, by extending ontology techniques to emergency decision-making and adopting the triangular framework for public safety, we establish a multidimensional information ontology for utility tunnel emergencies. Furthermore, a knowledge base for typical coupling accident evolution paths is established based on the event chain and contingency plan chain theory. Through integration with a multi-hazard accident basic database that serves the conditional, investigative and decision-making node within the evolution path, the simulation and deduction system is formulated, boasting a user-friendly visual interface, interactive functionality, and seamless applicability for widespread adoption. A case study demonstrates the system ability to support multiple paths and unified mapping deduction, offering practical emergency decision-making suggestions to mitigate cascading events in urban utility tunnels.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530322","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

Kinematic calibration of industrial robot using Bayesian modeling framework 利用贝叶斯建模框架对工业机器人进行运动学校准

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-06 DOI: 10.1016/j.ress.2024.110543

{"title":"Kinematic calibration of industrial robot using Bayesian modeling framework","authors":"","doi":"10.1016/j.ress.2024.110543","DOIUrl":"10.1016/j.ress.2024.110543","url":null,"abstract":"<div><div>Positioning accuracy of an end-effector is a crucial metric for evaluating industrial robot performance. Uncertainties in joint angles and joint backlash deviate actual angles from the designed nominal values to negate positioning accuracy. Most existing parameter identification methods overlook or not properly account for such uncertainties, leading to usually overconfident identification results. To this gap, the present study introduces a kinematic calibration methodology employing Bayesian parameter estimation to achieve identification of joint variables. New formulas based on data features of industrial robots for constructing the likelihood function are proposed, and model selection is applied to assess various likelihood functions for a tradeoff balance between complexity and accuracy. To evaluate the robustness of the proposed approach, identification of joint variables is conducted under different measurement noises. The position response of kinematic model is predicted based on the identified joint uncertainty information. The efficacy is verified through rigorous scrutiny involving both a numerical example and an engineering application. Results indicate that the proposed method exhibits satisfactory kinematic parameter identification accuracy and robustness. In addition, the uncertainty of parameters can be measured and the prediction of trajectory uncertainty intervals is realized simultaneously, which promotes the application of industrial robots in high-precision scenes.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530314","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

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

{"title":"Quantitative risk assessment for connected automated Vehicles: Integrating improved STPA-SafeSec and Bayesian network","authors":"","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":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":"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

Hierarchical structure-based model for importance and reliability assessment of water distribution networks 基于层次结构的配水管网重要性和可靠性评估模型

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI: 10.1016/j.ress.2024.110542

{"title":"Hierarchical structure-based model for importance and reliability assessment of water distribution networks","authors":"","doi":"10.1016/j.ress.2024.110542","DOIUrl":"10.1016/j.ress.2024.110542","url":null,"abstract":"<div><div>The Segment-Valve (SV) model can be utilized to analyze the reliability of water distribution networks (WDNs). However, it often focuses on the impact of segment failures on themselves. The isolation of segments in the WDNs not only affects the segments themselves but also influences other segments through which the water supply path passes. Therefore, considering the water supply path and the interaction between upstream and downstream segments, we convert the loops in the SV graph to nodes to simplify them, clearly describing the segmented hierarchy and its interconnections in a tree-like form, termed SV-tree. Based on the SV-tree and complex network theory, a method is proposed to estimate the supply shortage rate using betweenness centrality to provide a detailed analysis for local importance on example WDNs. Meanwhile, new analysis indicators that can reflect the global reliability of the WDNs are constructed from the mutual influence between segments and the difficulty for users to obtain water. The results demonstrate the efficacy of the new importance assessment indicator across various WDNs configurations, and its calculation time is much lower than that of hydraulic simulation. In addition, the reliability assessment indicators are more practical and can effectively identify problems existing in the WDNs.</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":"142421434","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

Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions 在不同工作条件下预测加工工具剩余使用寿命的渔业信息持续学习方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI: 10.1016/j.ress.2024.110549

{"title":"Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions","authors":"","doi":"10.1016/j.ress.2024.110549","DOIUrl":"10.1016/j.ress.2024.110549","url":null,"abstract":"<div><div>Accurate prediction of remaining useful life (RUL) of equipment has become an essential task in manufacturing. It not only helps prevent unexpected failures but also enables maximal utilization of available life, thus improving process efficiency. In practice, however, the use of multiple operating conditions that vary by time impedes efficient data-driven RUL prediction. Unlike conventional supervised learning setups, varying operating conditions generate heterogeneous data with time-varying generating distributions. Thus, existing approaches cannot be effectively applied due to increasing modeling and memory costs. One of the domains that suffer from this issue is machining, where RUL prediction of cutting tools is crucial for productivity. Considering realistic circumstances with varying operating conditions, this work proposes a method named Fisher-informed continual learning (FICL), which enables efficient tool RUL prediction that adaptively learns as conditions change without storing previous data and models. FICL uses Fisher information to improve generalization via sharpness-aware minimization and transfer knowledge between operating conditions through structural regularization. Experiments using datasets from real-world machining processes under five distinct operating conditions prove FICL’s efficacy, indicating its superior prediction performance to existing methods for all operating conditions. Particularly, FICL manifests the least catastrophic forgetting, implying it effectively retains informative knowledge from varying operating conditions.</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":"142442858","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

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

{"title":"Contrastive domain-invariant generalization for remaining useful life prediction under diverse conditions and fault modes","authors":"","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":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":"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

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

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

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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

{"title":"Adaptive support vector machine for time-variant failure probability function estimation","authors":"","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":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":"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