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

Probabilistic seismic risk analysis of electrical substations considering equipment-to-equipment seismic failure correlations 考虑设备间地震失效相关性的变电站概率地震风险分析

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-12 DOI: 10.1016/j.ress.2024.110588

{"title":"Probabilistic seismic risk analysis of electrical substations considering equipment-to-equipment seismic failure correlations","authors":"","doi":"10.1016/j.ress.2024.110588","DOIUrl":"10.1016/j.ress.2024.110588","url":null,"abstract":"<div><div>When an earthquake occurs, electrical equipment in a substation exhibits a certain level of seismic failure correlation since they suffer similar ground motions and share similar structural characteristics. However, this equipment-to-equipment seismic failure correlation (E2ESFC) was neglected in previous substation-level probabilistic seismic risk analyses due to the lack of awareness and practical approach. To investigate the effect of different degrees of the E2ESFC on the substation seismic risk, an efficient method for considering partially correlated seismic failure was proposed. The concepts of “damage demand probability” and “damage capacity probability” were derived from the equipment's fragility curve. Then the partial correlation of equipment's capacity probabilities can be easily introduced and incorporated into the substation-level risk analysis through the combination of Copula functions and the Monte Carlo simulation. A case study on a real-world 220/110 kV substation using an equi-correlation model demonstrated that ignoring the E2ESFC among the same type of equipment will lead to an underestimate of the probability of seeing high seismic loss. Furthermore, a general method to assess the E2ESFC coefficients between equipment was also proposed, laying the foundation to facilitate applications of the introduced E2ESFC simulation method and to generate a more reliable system risk assessment result.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446048","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

Fragility estimation for performance-based structural design of floating offshore wind turbine components 基于性能的浮式海上风力涡轮机部件结构设计的易损性评估

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-12 DOI: 10.1016/j.ress.2024.110587

{"title":"Fragility estimation for performance-based structural design of floating offshore wind turbine components","authors":"","doi":"10.1016/j.ress.2024.110587","DOIUrl":"10.1016/j.ress.2024.110587","url":null,"abstract":"<div><div>This study proposes a computational and mathematical framework aimed at assessing the reliability of structural components within Floating Offshore Wind Turbines (FOWT) that reflects the various sources of uncertainties coupled between structural analyses, hydrodynamics, and aerodynamics. The limit state functions are represented through structural capacity and environmental demand models for selected structural failure modes that incorporate fully coupled aero-hydro-servo-elastic analysis. The fragility surfaces are developed for a selected benchmark wind turbine for both operating and parking conditions. The fragilities are also estimated under 50-year and 100-year environmental conditions in the selected U.S. coastal regions. It is found that the wind speed variations largely affect the fragility during non-operation, while wave height variations are significant during operation. Increased uncertainties in environmental parameters raised failure probabilities, especially in lower fragility ranges targeted by design codes. Analyses in U.S. coastal environments show both parking and operating conditions can be critical, challenging the previous focus on parking. Sensitivity studies reveal that under mild conditions, structural reliability is influenced by moment of inertia and material strength, but as environmental loads increase, these parameters become equally significant. Increased uncertainties in parameters lead to higher failure risks, especially below 25 m/s wind speeds.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530218","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

Machine learning-based outlier detection for pipeline in-line inspection data 基于机器学习的管道在线检测数据离群点检测

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-12 DOI: 10.1016/j.ress.2024.110553

{"title":"Machine learning-based outlier detection for pipeline in-line inspection data","authors":"","doi":"10.1016/j.ress.2024.110553","DOIUrl":"10.1016/j.ress.2024.110553","url":null,"abstract":"<div><div>Pipeline companies are facing challenges in maintaining the integrity and reliability of their pipelines. They are working towards predictive maintenance using machine learning-based approaches to predicting anomalies. Training machine learning models requires sufficient data. Data quality is therefore becoming important because inaccurate data will lead to an inaccurate or wrong decision on pipeline condition assessment and the following management. This research paper intends to address the data quality issues of pipeline inspection data such as in-line inspection (ILI) data using machine learning models. Different machine learning models developed by random forest regression, linear regression, and nearest neighbors’ methods were tested to detect outliers in the ILI data. In this paper, the ILI data collected from an oil pipeline over a period of 22 years was applied to testing and analysis. To verify the outlier detection results of machine learning models, we used statistical analysis including Z-score method to check and find if there are any gaps in the analysis. It verifies that all these methods show almost the same or very similar results for the detection of the outliers. Hence, this study presents a robust method for the field applications in the pipeline industry.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527450","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

Resilience-Based Restoration Model for Optimizing Corrosion Repair Strategies in Tunnel Lining 优化隧道衬砌腐蚀修复策略的复原力修复模型

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110546

{"title":"Resilience-Based Restoration Model for Optimizing Corrosion Repair Strategies in Tunnel Lining","authors":"","doi":"10.1016/j.ress.2024.110546","DOIUrl":"10.1016/j.ress.2024.110546","url":null,"abstract":"<div><div>In tunnel engineering, the corrosion of steel rebar is a critical factor leading to structural degradation and failure, causing a decline in load-bearing capacity, deformation, and cracking. For decision-makers, identifying the optimal timing for tunnel maintenance and selecting effective repair strategies is of paramount importance. This study introduces a resilience-based restoration model to analyze tunnel failure due to corrosion throughout its service life and to optimize the timing and selection of maintenance strategies. The model generates time-variant failure curves by constructing limit equilibrium equations. The entropy weight method is employed to quantify and weight the impact of various failure modes, determining the timing for maintenance when the failure curve exceeds a predefined threshold. Additionally, the model's uncertainty is effectively reduced through regular inspections and Bayesian updating methods, enhancing prediction accuracy. The study further incorporates a resilience index and a benefit index to provide a quantitative assessment of maintenance plans, assisting decision-makers in selecting the optimal strategy. By exemplifying the model with a case study of steel rebar corrosion in a tunnel, this paper demonstrates the model's applicability and offers a new scientific approach for quantitative maintenance decision-making in tunnel engineering.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442856","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

Efficient reliability analysis of generalized k-out-of-n phased-mission systems 广义 k-out-of-n 相位任务系统的高效可靠性分析

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110581

{"title":"Efficient reliability analysis of generalized k-out-of-n phased-mission systems","authors":"","doi":"10.1016/j.ress.2024.110581","DOIUrl":"10.1016/j.ress.2024.110581","url":null,"abstract":"<div><div>A <span><math><mi>k</mi></math></span>-out-of-<span><math><mi>n</mi></math></span> phased-mission system (PMS) is a PMS where the system structure is <span><math><mi>k</mi></math></span>-out-of-<span><math><mi>n</mi></math></span>: G in each phase. This paper investigates <span><math><mi>k</mi></math></span>-out-of-<span><math><mi>n</mi></math></span> PMSs with phase-<em>K</em>-out-of-<em>N</em> requirement, where the entire mission is successful if at least <em>K</em> out of the <em>N</em> phases achieve success. Such system is referred to as a generalized <span><math><mi>k</mi></math></span>-out-of-<span><math><mi>n</mi></math></span> PMS (<span><math><mi>k</mi></math></span>/<span><math><mi>n</mi></math></span>-GPMS). The <span><math><mi>k</mi></math></span>/<span><math><mi>n</mi></math></span>-GPMSs are prevalent in applications such as satellites, unmanned aerial vehicles (UAVs), wireless sensor networks and so on. In this paper, a novel method based on multi-valued decision diagram (MDD) is proposed to analyze the reliability of <span><math><mi>k</mi></math></span>/<span><math><mi>n</mi></math></span>-GPMSs, where the number of available components <em>n</em>, the required number of components <em>k</em>, and the components failure behaviors in different phases may vary. Distinguishing from the traditional phase-by-phase MDD generation method, the proposed method considers the behavior of all phases simultaneously and generates only one MDD model in a top-down manner. To illustrate the application of the proposed method, the reliability and the sensitivity of a four UAVs system which conducts supplies delivery mission is analyzed. The complexity analysis is performed. The correctness and efficiency are verified and demonstrated by several case studies. The proposed method is also compared with Monte Carlo simulation method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446050","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 benchmark on uncertainty quantification for deep learning prognostics 深度学习预报学的不确定性量化基准

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110513

{"title":"A benchmark on uncertainty quantification for deep learning prognostics","authors":"","doi":"10.1016/j.ress.2024.110513","DOIUrl":"10.1016/j.ress.2024.110513","url":null,"abstract":"<div><div>Reliable uncertainty quantification on RUL prediction is crucial for informative decision-making in predictive maintenance. In this context, we assess some of the latest developments in the field of uncertainty quantification for deep learning prognostics. This includes the state-of-the-art variational inference algorithms for Bayesian neural networks (BNN) as well as popular alternatives such as Monte Carlo Dropout (MCD), deep ensembles (DE), and heteroscedastic neural networks (HNN). All the inference techniques share the same inception architecture as functional model. The performance of the methods is evaluated on a subset of the large NASA N-CMAPSS dataset for aircraft engines. The assessment includes RUL prediction accuracy, the quality of predictive uncertainty, and the possibility of breaking down the total predictive uncertainty into its aleatoric and epistemic parts. Although all methods are close in terms of accuracy, we find differences in the way they estimate uncertainty. Thus, DE and MCD generally provide more conservative predictive uncertainty than BNN. Surprisingly, HNN achieve strong results without the added complexity of BNN. None of these methods exhibited strong robustness to out-of-distribution cases, with BNN and HNN methods particularly susceptible to low accuracy and overconfidence. BNN techniques presented anomalous miscalibration issues at the later stages of the system lifetime.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446047","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 integrated method of extended STPA and BN for safety assessment of man-machine phased-mission system 用于人机相控任务系统安全评估的扩展 STPA 和 BN 综合方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110569

{"title":"An integrated method of extended STPA and BN for safety assessment of man-machine phased-mission system","authors":"","doi":"10.1016/j.ress.2024.110569","DOIUrl":"10.1016/j.ress.2024.110569","url":null,"abstract":"<div><div>Man-Machine Phased-Mission System (MMPMS) usually demands the cooperation of operators with different responsibilities and machines to accomplish multi-phase missions. Its machine configuration and human organization structure may change across phases, and phase dependencies of machine failures and human errors may exist. In current studies, the safety of man-machine system is usually analyzed qualitatively by System Theoretic Process Analysis (STPA) and assessed quantitatively by the integration of STPA with Bayesian Networks (BN). These studies only focus on single-phase systems and conduct single-phase BN while cannot address the features of MMPMS. In this paper, a qualitative analysis and quantitative assessment method for phase dependencies is proposed and integrated into the method that combines STPA and BN. Firstly, four types of phase dependencies in MMPMS are identified. Secondly, new mapping rules for phase dependencies are proposed to integrate single-phase BN into a multi-phase BN. Thirdly, the quantitative assessment method for phase dependencies considering the effects of human organization structure changes are proposed to quantify the parameters of multi-phase BN. Fourthly, the safety of MMPMS can be assessed through multi-phase BN. Finally, an Unmanned Aerial Vehicle system with three-phase missions is presented as a case study to demonstrate the effectiveness of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442946","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

Spatial network disintegration based on spatial coverage 基于空间覆盖的空间网络分解

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110525

{"title":"Spatial network disintegration based on spatial coverage","authors":"","doi":"10.1016/j.ress.2024.110525","DOIUrl":"10.1016/j.ress.2024.110525","url":null,"abstract":"<div><div>The problem of network disintegration, such as interrupting rumor spreading networks and dismantling terrorist networks, involves evaluating changes in network performance. However, traditional metrics primarily focus on the topological structure and often neglect the crucial spatial attributes of nodes and edges, thereby failing to capture the spatial functional losses. Here we first introduce the concept of spatial coverage to evaluate the spatial network performance, which is defined as the convex hull area of the largest connected component. Then a greedy algorithm is proposed to maximize the reduction of the convex hull area through strategic node removals. Extensive experiments verified that the spatial coverage metric can effectively quantify changes in the performance of spatial networks, and the proposed algorithm can maximize the reduction of the convex hull area of the largest connected component compared to genetic algorithm and centrality strategies. Specifically, our algorithm reduces the convex hull area by up to 30% compared to the best-performing strategy. These results indicate that the critical nodes influencing network performance are a combination of numerous peripheral spatial leaf nodes and a few central spatial core nodes. This study substantially enhances our understanding of spatial network robustness and provides a novel perspective for network optimization.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442857","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 evaluation of multi-feature system based on hidden Markov model 基于隐马尔可夫模型的多特征系统复原力评估

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110561

引用次数: 0

Dynamic risk assessment for process operational safety based on reachability analysis 基于可达性分析的工艺运行安全动态风险评估

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110564

引用次数: 0