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

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 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2024-10-12 DOI: 10.1016/j.ress.2024.110553

Muhammad Hussain, Tieling Zhang

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.

管道公司在维护管道完整性和可靠性方面面临挑战。他们正在利用基于机器学习的方法来预测异常情况，从而实现预测性维护。训练机器学习模型需要足够的数据。因此，数据质量变得越来越重要，因为不准确的数据会导致管道状况评估和后续管理决策的不准确或错误。本研究论文旨在利用机器学习模型解决管道检测数据（如在线检测 (ILI) 数据）的数据质量问题。本文测试了由随机森林回归、线性回归和最近邻方法开发的不同机器学习模型，以检测 ILI 数据中的异常值。本文将从一条输油管道收集到的长达 22 年的 ILI 数据用于测试和分析。为了验证机器学习模型的离群值检测结果，我们使用了统计分析方法，包括 Zcore 方法，以检查和发现分析中是否存在任何漏洞。结果表明，所有这些方法对异常值的检测结果几乎相同或非常相似。因此，本研究提出了一种适用于管道行业现场应用的稳健方法。

{"title":"Machine learning-based outlier detection for pipeline in-line inspection data","authors":"Muhammad Hussain, Tieling Zhang","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":"254 ","pages":"Article 110553"},"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 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110546

Qian Zhang , Yaoqi Nie , Yanliang Du , Weigang Zhao , Shujie Cao

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.

在隧道工程中，钢筋腐蚀是导致结构退化和失效的关键因素，会造成承载能力下降、变形和开裂。对于决策者来说，确定隧道维护的最佳时机并选择有效的修复策略至关重要。本研究引入了一个基于复原力的修复模型，用于分析隧道在整个使用寿命期间因腐蚀而导致的故障，并优化维护策略的时机和选择。该模型通过构建极限平衡方程生成时变失效曲线。采用熵权法对各种失效模式的影响进行量化和加权，当失效曲线超过预定阈值时确定维护时机。此外，通过定期检查和贝叶斯更新方法，有效减少了模型的不确定性，提高了预测精度。研究还进一步纳入了弹性指数和效益指数，对维护计划进行量化评估，帮助决策者选择最佳策略。通过对隧道钢筋腐蚀的案例研究，本文展示了该模型的适用性，为隧道工程的定量维护决策提供了一种新的科学方法。

{"title":"Resilience-Based Restoration Model for Optimizing Corrosion Repair Strategies in Tunnel Lining","authors":"Qian Zhang , Yaoqi Nie , Yanliang Du , Weigang Zhao , Shujie Cao","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":"253 ","pages":"Article 110546"},"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 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110581

Guizhuang Chen , Yuliang Hu , Chaonan Wang , Zhitao Wu , Wenjing Rong , Quanlong Guan

A

k

-out-of-

n

phased-mission system (PMS) is a PMS where the system structure is

k

-out-of-

n

: G in each phase. This paper investigates

k

-out-of-

n

PMSs with phase-K-out-of-N requirement, where the entire mission is successful if at least K out of the N phases achieve success. Such system is referred to as a generalized

k

-out-of-

n

PMS (

k

/

n

-GPMS). The

k

/

n

-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

k

/

n

-GPMSs, where the number of available components n, the required number of components k, 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.

k-out-of-n 分阶段任务系统（PMS）是指系统结构为 k-out-of-n 的分阶段任务系统：G 的分阶段系统。本文研究的 k-out-of-n PMS 具有阶段-K-out-of-N 的要求，即 N 个阶段中至少有 K 个阶段取得成功，整个任务才算成功。这种系统被称为广义 k-out-of-n PMS（k/n-GPMS）。k/n-GPMS 在卫星、无人飞行器（UAV）、无线传感器网络等应用中非常普遍。本文提出了一种基于多值决策图（MDD）的新方法，用于分析 k/n-GPMS 的可靠性，其中可用组件数 n、所需组件数 k 以及组件在不同阶段的失效行为可能各不相同。有别于传统的逐阶段 MDD 生成方法，所提出的方法同时考虑了所有阶段的行为，以自上而下的方式只生成一个 MDD 模型。为了说明所提方法的应用，分析了执行物资运送任务的四架无人机系统的可靠性和灵敏度。进行了复杂性分析。几项案例研究验证并证明了该方法的正确性和高效性。此外，还将提出的方法与蒙特卡罗模拟方法进行了比较。

{"title":"Efficient reliability analysis of generalized k-out-of-n phased-mission systems","authors":"Guizhuang Chen , Yuliang Hu , Chaonan Wang , Zhitao Wu , Wenjing Rong , Quanlong Guan","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":"253 ","pages":"Article 110581"},"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

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 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110569

Xin Lu , Shengkui Zeng , Jianbin Guo , Wei Deng , Mingjun He , Haiyang Che

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.

人机分阶段任务系统（MMPMS）通常要求不同职责的操作员与机器合作完成多阶段任务。其机器配置和人员组织结构可能在不同阶段发生变化，机器故障和人为错误可能存在阶段依赖性。目前的研究通常通过系统理论过程分析法（STPA）对人机系统的安全性进行定性分析，并通过系统理论过程分析法与贝叶斯网络（BN）的结合对人机系统的安全性进行定量评估。这些研究仅关注单相系统，并进行单相贝叶斯网络，而无法解决人机管理系统的特点。本文提出了相依性的定性分析和定量评估方法，并将 STPA 和 BN 结合在一起。首先，确定了 MMPMS 中的四种相位依赖关系。其次，提出了新的相依性映射规则，将单相 BN 整合到多相 BN 中。第三，提出了考虑人体组织结构变化影响的相依性定量评估方法，以量化多相 BN 的参数。第四，通过多相 BN 评估多用途军事管理信息系统的安全性。最后，以一个三阶段任务的无人机系统为例，展示了所提方法的有效性。

{"title":"An integrated method of extended STPA and BN for safety assessment of man-machine phased-mission system","authors":"Xin Lu , Shengkui Zeng , Jianbin Guo , Wei Deng , Mingjun He , Haiyang Che","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":"253 ","pages":"Article 110569"},"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

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 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110513

Luis Basora , Arthur Viens , Manuel Arias Chao , Xavier Olive

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.

可靠的 RUL 预测不确定性量化对于预测性维护中的信息决策至关重要。在此背景下，我们评估了深度学习预测不确定性量化领域的一些最新进展。这包括最先进的贝叶斯神经网络（BNN）变异推理算法，以及蒙特卡罗剔除（MCD）、深度集合（DE）和异塞神经网络（HNN）等流行的替代算法。所有推理技术都采用与功能模型相同的初始架构。这些方法的性能在 NASA N-CMAPSS 飞机发动机大型数据集的子集上进行了评估。评估内容包括 RUL 预测精度、预测不确定性的质量，以及将总预测不确定性分解为可知部分和认识部分的可能性。尽管所有方法在准确性方面都很接近，但我们发现它们在估计不确定性的方式上存在差异。因此，DE 和 MCD 通常比 BNN 提供更保守的预测不确定性。令人惊讶的是，HNN 在不增加 BNN 复杂性的情况下取得了很好的结果。这些方法都没有表现出对分布外情况的强大鲁棒性，BNN 和 HNN 方法尤其容易受到低准确度和过度自信的影响。BNN 技术在系统寿命的后期阶段出现了异常误判问题。

{"title":"A benchmark on uncertainty quantification for deep learning prognostics","authors":"Luis Basora , Arthur Viens , Manuel Arias Chao , Xavier Olive","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":"253 ","pages":"Article 110513"},"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

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 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110525

Ye Deng , Zhigang Wang , Yu Xiao , Xiaoda Shen , Jürgen Kurths , Jun Wu

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.

网络瓦解问题，如中断谣言传播网络和瓦解恐怖主义网络，涉及评估网络性能的变化。然而，传统指标主要关注拓扑结构，往往忽视节点和边的关键空间属性，因而无法捕捉空间功能损失。在此，我们首先引入空间覆盖率的概念来评估空间网络性能，空间覆盖率定义为最大连通组件的凸壳面积。然后，我们提出了一种贪婪算法，通过策略性地删除节点，最大限度地减少凸壳面积。广泛的实验验证了空间覆盖度指标能有效量化空间网络性能的变化，与遗传算法和中心性策略相比，所提出的算法能最大限度地缩小最大连通分量的凸壳面积。具体来说，与表现最好的策略相比，我们的算法最多可减少 30% 的凸壳面积。这些结果表明，影响网络性能的关键节点是众多外围空间叶节点和少数中心空间核心节点的组合。这项研究大大增强了我们对空间网络鲁棒性的理解，并为网络优化提供了一个新的视角。

{"title":"Spatial network disintegration based on spatial coverage","authors":"Ye Deng , Zhigang Wang , Yu Xiao , Xiaoda Shen , Jürgen Kurths , Jun Wu","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":"253 ","pages":"Article 110525"},"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 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110561

Jiaying Liu , Jun Zhang , Qingfeng Tian , Bei Wu

Modern systems have become increasingly vulnerable to threats due to their growing complexity nowadays. Multi-feature systems, prevalent in the realm of complex structures, manifest their performance through a diverse array of features. In response to threats, this paper develops a resilience evaluation model for multi-feature systems based on hidden Markov models, which can describe the dynamic relationship between performance levels and external features. Quantitative resilience indicators are presented across three distinct dimensions: resistant, absorption, and recovery, whose analytical formulas are derived by generating functions and properties are proved. Meanwhile, simulation algorithms are proposed to verify the correctness of the analytic formulas. Finally, taking the system under the threat of flood disasters as an example, the resilience model proposed in this paper is applied to evaluate its resilience, and the robustness of the resilience evaluation indicators is verified.

由于现代系统日益复杂，它们越来越容易受到威胁。多特征系统是复杂结构领域的普遍现象，通过一系列不同的特征来体现其性能。为应对威胁，本文基于隐马尔可夫模型开发了多特征系统的弹性评估模型，该模型可描述性能水平与外部特征之间的动态关系。该模型可描述性能水平与外部特征之间的动态关系。本文从三个不同的维度提出了定量弹性指标：抵抗、吸收和恢复，并通过生成函数推导出其解析公式，证明了其属性。同时，提出了仿真算法来验证解析公式的正确性。最后，以洪水灾害威胁下的系统为例，应用本文提出的复原力模型对其复原力进行评价，验证了复原力评价指标的稳健性。

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 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110564

Yuchen Wang , Zuzhen Ji , Yi Cao , Shuang-Hua Yang

The successful implementation of chemical production systems necessitates an effective mechanism for quantitatively assessing dynamic risk. Current methods predominantly evaluate the entire industrial process – from basic operations to the safety protection layer – and typically focus on the impact of fixed deviations in process parameters on the development of abnormal conditions. However, the cumulative impact of process disturbances on dynamic risk deserves attention, particularly in the context of abnormal operating conditions. To overcome the limitations of existing methodologies, this paper introduces a suite of novel dynamic operational risk indices based on reachability analysis, encapsulated within a comprehensive framework that includes identifying safety critical variables and quantifying uncertainties in set-form. The efficacy of the proposed method is demonstrated through applications to a tank system and a Continuous Stirred Tank Reactor (CSTR) system. This approach has the potential to enhance industry understanding of failure mechanisms and to foster the development of preventative and mitigative strategies.

要成功实施化工生产系统，就必须建立一个有效的动态风险定量评估机制。目前的方法主要评估整个工业流程--从基本操作到安全保护层--通常侧重于流程参数的固定偏差对异常情况发展的影响。然而，工艺干扰对动态风险的累积影响值得关注，尤其是在异常运行条件下。为了克服现有方法的局限性，本文介绍了一套基于可达性分析的新型动态运行风险指数，该指数包含在一个综合框架内，其中包括识别安全关键变量和以集合形式量化不确定性。通过对储罐系统和连续搅拌罐反应器（CSTR）系统的应用，证明了所提方法的有效性。这种方法有可能提高行业对失效机制的理解，并促进预防和缓解策略的发展。

{"title":"Dynamic risk assessment for process operational safety based on reachability analysis","authors":"Yuchen Wang , Zuzhen Ji , Yi Cao , Shuang-Hua Yang","doi":"10.1016/j.ress.2024.110564","DOIUrl":"10.1016/j.ress.2024.110564","url":null,"abstract":"<div><div>The successful implementation of chemical production systems necessitates an effective mechanism for quantitatively assessing dynamic risk. Current methods predominantly evaluate the entire industrial process – from basic operations to the safety protection layer – and typically focus on the impact of fixed deviations in process parameters on the development of abnormal conditions. However, the cumulative impact of process disturbances on dynamic risk deserves attention, particularly in the context of abnormal operating conditions. To overcome the limitations of existing methodologies, this paper introduces a suite of novel dynamic operational risk indices based on reachability analysis, encapsulated within a comprehensive framework that includes identifying safety critical variables and quantifying uncertainties in set-form. The efficacy of the proposed method is demonstrated through applications to a tank system and a Continuous Stirred Tank Reactor (CSTR) system. This approach has the potential to enhance industry understanding of failure mechanisms and to foster the development of preventative and mitigative strategies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110564"},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442948","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-valued decision diagrams-based method for reliability analysis of performance-sharing k-out-of-n: G system considering component degradation 基于多值决策图的性能共享 k-out-of-n. G 系统可靠性分析方法考虑组件退化的 G 系统

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2024-10-11 DOI: 10.1016/j.ress.2024.110531

Tianyuan Zhang , Liudong Xing , Yuchang Mo

This paper models the reliability of a performance-sharing k-out-of-n: G system with heterogeneous degrading components and a performance-redistributing common bus. Each component may behave at various performance levels to meet its random demand. If one component exhibits performance beyond its demand, the redundant performance is redistributed to components with deficit performance via the common bus with limited capacity. The system fails if the number of operating components is less than k after sharing the redundant performance. A new analytical method based on multi-valued decision diagrams (MDDs) is put forward, which comprises an efficient model generation algorithm leveraging top-down simplification rules and a new ordering heuristic for improving MDD generation efficiency. The MDD evaluation engages the continuous-time Markov Chains to compute the steady-state probabilities of system components considering the degradation effects. Case studies of a wind power generation system and a data processing system as well as benchmark studies are conducted to illustrate the applicability and efficiency of the proposed method. A comparative study with the universal generating function-based method is also provided to further demonstrate the efficiency of the proposed MDD-based method.

本文模拟了一个性能共享的 k-out-of-n：G 系统的可靠性建模，该系统具有异构的性能下降组件和性能再分配公共总线。每个组件都可能表现出不同的性能水平，以满足其随机需求。如果某个组件表现出超出其需求的性能，冗余性能将通过容量有限的公共总线重新分配给性能不足的组件。共享冗余性能后，如果运行组件的数量少于 k，系统就会失效。本文提出了一种基于多值决策图（MDD）的新分析方法，包括一种利用自上而下简化规则的高效模型生成算法和一种用于提高 MDD 生成效率的新排序启发式。MDD 评估使用连续时间马尔可夫链计算系统组件的稳态概率，同时考虑退化效应。对风力发电系统和数据处理系统进行了案例研究和基准研究，以说明所提方法的适用性和效率。此外，还提供了与基于通用生成函数方法的比较研究，以进一步证明所提出的基于 MDD 方法的效率。

{"title":"A multi-valued decision diagrams-based method for reliability analysis of performance-sharing k-out-of-n: G system considering component degradation","authors":"Tianyuan Zhang , Liudong Xing , Yuchang Mo","doi":"10.1016/j.ress.2024.110531","DOIUrl":"10.1016/j.ress.2024.110531","url":null,"abstract":"<div><div>This paper models the reliability of a performance-sharing <em>k</em>-out-of-<em>n</em>: G system with heterogeneous degrading components and a performance-redistributing common bus. Each component may behave at various performance levels to meet its random demand. If one component exhibits performance beyond its demand, the redundant performance is redistributed to components with deficit performance via the common bus with limited capacity. The system fails if the number of operating components is less than <em>k</em> after sharing the redundant performance. A new analytical method based on multi-valued decision diagrams (MDDs) is put forward, which comprises an efficient model generation algorithm leveraging top-down simplification rules and a new ordering heuristic for improving MDD generation efficiency. The MDD evaluation engages the continuous-time Markov Chains to compute the steady-state probabilities of system components considering the degradation effects. Case studies of a wind power generation system and a data processing system as well as benchmark studies are conducted to illustrate the applicability and efficiency of the proposed method. A comparative study with the universal generating function-based method is also provided to further demonstrate the efficiency of the proposed MDD-based method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110531"},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530319","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

Research on Scenario Extrapolation and Emergency Decision-Making for Fire and Explosion Accidents at University Laboratories Based on BN-CBR 基于 BN-CBR 的大学实验室火灾和爆炸事故情景推断与应急决策研究

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2024-10-10 DOI: 10.1016/j.ress.2024.110579

Jie Liu , Fei Cai , Wanqing Wang , Haoyuan Zhu , Liangyun Teng , Xuehua Luo , Yi Chen , Chenwei Hao

To solve the problems of suddenness, uncertainty and untimely emergency decision-making related to fire and explosion accidents in university laboratories, a combined method of BN and CBR is introduced to analyze laboratory accidents. By summarizing the characteristics of 72 accident cases worldwide, four scenario elements with key roles are extracted by combining the public safety triangle theoretical model; a BN is established from the macro perspective, which is based on the construction of dynamic scenarios; the evolution path is analyzed via BN theory; and the probability of occurrence of accidents is quantified from the microscopic perspective, with a focus on the analysis of the accidental evolution process. A case similarity calculation is carried out via CBR, and the construction of a BN-CBR-assisted decision-making model is completed, verified and corrected in an case study. The results show that the BN-CBR model can quickly determine the accident evolution path and the most similar historical cases, and its quantitative probability calculation enables one to comprehensively grasp the real-time state of the whole accident and the emergency response in a timely manner, which provides a new way to approach emergency decision-making of accidents.

为解决高校实验室火灾爆炸事故的突发性、不确定性和应急决策不及时等问题，引入BN和CBR相结合的方法对实验室事故进行分析。通过总结全球 72 起事故案例的特点，结合公共安全三角理论模型，提取了四个具有关键作用的情景要素；从宏观角度建立了基于动态情景构建的 BN；通过 BN 理论分析了演化路径；从微观角度量化了事故发生概率，重点分析了事故演化过程。通过 CBR 进行案例相似性计算，在案例研究中完成 BN-CBR 辅助决策模型的构建、验证和修正。结果表明，BN-CBR模型能够快速确定事故演化路径和最相似的历史案例，其定量概率计算能够及时全面地掌握整个事故的实时状态和应急响应，为事故应急决策提供了新的思路。

{"title":"Research on Scenario Extrapolation and Emergency Decision-Making for Fire and Explosion Accidents at University Laboratories Based on BN-CBR","authors":"Jie Liu , Fei Cai , Wanqing Wang , Haoyuan Zhu , Liangyun Teng , Xuehua Luo , Yi Chen , Chenwei Hao","doi":"10.1016/j.ress.2024.110579","DOIUrl":"10.1016/j.ress.2024.110579","url":null,"abstract":"<div><div>To solve the problems of suddenness, uncertainty and untimely emergency decision-making related to fire and explosion accidents in university laboratories, a combined method of BN and CBR is introduced to analyze laboratory accidents. By summarizing the characteristics of 72 accident cases worldwide, four scenario elements with key roles are extracted by combining the public safety triangle theoretical model; a BN is established from the macro perspective, which is based on the construction of dynamic scenarios; the evolution path is analyzed via BN theory; and the probability of occurrence of accidents is quantified from the microscopic perspective, with a focus on the analysis of the accidental evolution process. A case similarity calculation is carried out via CBR, and the construction of a BN-CBR-assisted decision-making model is completed, verified and corrected in an case study. The results show that the BN-CBR model can quickly determine the accident evolution path and the most similar historical cases, and its quantitative probability calculation enables one to comprehensively grasp the real-time state of the whole accident and the emergency response in a timely manner, which provides a new way to approach emergency decision-making of accidents.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110579"},"PeriodicalIF":9.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420980","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