Reliability Engineering & System Safety最新文献_第3页

Efficient global reliability sensitivity method by combining dimensional reduction integral with stochastic collocation

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-03 DOI: 10.1016/j.ress.2025.110993

Xiaomin Wu, Zhenzhou Lu

Defined as the mean square difference between unconditional failure probability (FP) and conditional FP on fixed input realization, global reliability sensitivity (GRS) can quantify the effect of random input on FP. For efficiently estimating the GRS, a novel method is proposed by combining truncated dimensional reduction integral with stochastic collocation (DRI-SC). In the DRI-SC, the unconditional and conditional FPs are equivalently converted into the expected cumulative distribution function (CDF) of a selected reduction input. Then, using the continuity of CDF, a truncated DRI is combined with SC to efficiently estimate the expected CDF. To further enhance the efficiency of DRI-SC, an adaptive Kriging model is trained to provide the integrand CDF values at the SC nodes. The novelties of the DRI-SC include deriving the unconditional and conditional FPs required by GRS as the expected CDF, designing an SC node-sharing strategy, and training the Kriging model in the SC node set. DRI-SC inherits the universality of numerical simulation but avoids its prohibitive computation, and the DRI-SC maintains the efficiency of the existing SC-based GRS methods but avoids the density fitting. The superiority of the DRI-SC over existing methods is verified by the presented examples.

{"title":"Efficient global reliability sensitivity method by combining dimensional reduction integral with stochastic collocation","authors":"Xiaomin Wu, Zhenzhou Lu","doi":"10.1016/j.ress.2025.110993","DOIUrl":"10.1016/j.ress.2025.110993","url":null,"abstract":"<div><div>Defined as the mean square difference between unconditional failure probability (FP) and conditional FP on fixed input realization, global reliability sensitivity (GRS) can quantify the effect of random input on FP. For efficiently estimating the GRS, a novel method is proposed by combining truncated <strong>d</strong>imensional <strong>r</strong>eduction <strong>i</strong>ntegral with <strong>s</strong>tochastic <strong>c</strong>ollocation (DRI-SC). In the DRI-SC, the unconditional and conditional FPs are equivalently converted into the expected cumulative distribution function (CDF) of a selected reduction input. Then, using the continuity of CDF, a truncated DRI is combined with SC to efficiently estimate the expected CDF. To further enhance the efficiency of DRI-SC, an adaptive Kriging model is trained to provide the integrand CDF values at the SC nodes. The novelties of the DRI-SC include deriving the unconditional and conditional FPs required by GRS as the expected CDF, designing an SC node-sharing strategy, and training the Kriging model in the SC node set. DRI-SC inherits the universality of numerical simulation but avoids its prohibitive computation, and the DRI-SC maintains the efficiency of the existing SC-based GRS methods but avoids the density fitting. The superiority of the DRI-SC over existing methods is verified by the presented examples.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110993"},"PeriodicalIF":9.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578813","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

Enhanced risk assessment framework for complex maritime traffic systems via data driven: A case study of ship navigation in Arctic

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-03 DOI: 10.1016/j.ress.2025.110991

Shenping Hu , Cuiwen Fang , Jianjun Wu , Cunlong Fan , Xinxin Zhang , Xue Yang , Bing Han

The era of big data has been characterized by an increasing diversity of information and a deeper application of system safety. In this context, this study proposes an enhanced risk assessment (ERA) framework to estimate traffic risk from massive data obtained in complex maritime traffic systems. The ERA framework adopts a 4R model that includes risk perception, risk cognition, risk reasoning, and risk control. The ERA framework integrates the Systems Theoretic Accident Model and Process and Stochastic Petri Nets to analyze the ship traffic process and develop risk control schemes. The feasibility of the proposed framework is demonstrated by a case study in Arctic waters. The results indicate that ice concentration represents a key factor for ship traffic in Arctic waters and that the risk control scheme type is related to the ice resistance level of ships. Accordingly, for ships with low ice resistance or no ice-class ships, the traffic risk is high when they are passing through the East Siberian, Laptev, Kara Sea, and the Vilkitskogo Strait, and icebreakers are required in July and October. In contrast, for ships with a higher ice resistance, regular traffic is generally possible for the East Siberian and Laptev Seas.

{"title":"Enhanced risk assessment framework for complex maritime traffic systems via data driven: A case study of ship navigation in Arctic","authors":"Shenping Hu , Cuiwen Fang , Jianjun Wu , Cunlong Fan , Xinxin Zhang , Xue Yang , Bing Han","doi":"10.1016/j.ress.2025.110991","DOIUrl":"10.1016/j.ress.2025.110991","url":null,"abstract":"<div><div>The era of big data has been characterized by an increasing diversity of information and a deeper application of system safety. In this context, this study proposes an enhanced risk assessment (ERA) framework to estimate traffic risk from massive data obtained in complex maritime traffic systems. The ERA framework adopts a 4R model that includes risk perception, risk cognition, risk reasoning, and risk control. The ERA framework integrates the Systems Theoretic Accident Model and Process and Stochastic Petri Nets to analyze the ship traffic process and develop risk control schemes. The feasibility of the proposed framework is demonstrated by a case study in Arctic waters. The results indicate that ice concentration represents a key factor for ship traffic in Arctic waters and that the risk control scheme type is related to the ice resistance level of ships. Accordingly, for ships with low ice resistance or no ice-class ships, the traffic risk is high when they are passing through the East Siberian, Laptev, Kara Sea, and the Vilkitskogo Strait, and icebreakers are required in July and October. In contrast, for ships with a higher ice resistance, regular traffic is generally possible for the East Siberian and Laptev Seas.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110991"},"PeriodicalIF":9.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563191","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

DCAGGCN: A novel method for remaining useful life prediction of bearings

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-03 DOI: 10.1016/j.ress.2025.110978

Deqiang He , Jiayang Zhao , Zhenzhen Jin , Chenggeng Huang , Cai Yi , Jinxin Wu

Accurate prediction of Bearings' remaining useful life (RUL) is crucial in equipment operation and maintenance. The bearing RUL prediction technology based on GCN has recently been widely used. However, the existing GCN-based RUL prediction results are limited by two aspects : (1) GCN usually uses the predefined adjacency matrix to define the graph, which makes the graph unable to track the real-time correlation of degradation features in time. (2) Existing GCN uses only one to two layers of graph convolution and cannot extract deep features. Based on the issues above, this paper proposes a bearing RUL prediction model that utilizes a Dual-correlation adaptive gated graph convolutional network (DCAGGCN). Firstly, a predefined double correlation graph is proposed and obtained by feature channel data. Next, an adaptive graph is created by transforming a source matrix and a target matrix, and then integrating it with a predefined graph. This allows the network to consider two types of correlation and adaptively adjust the graph's topology. In addition, this paper proposes a gated convolution layer, which can greatly alleviate the over-smoothing problem caused by the stacking of graph convolution layers. The effectiveness of the proposed method is verified by two public datasets.

{"title":"DCAGGCN: A novel method for remaining useful life prediction of bearings","authors":"Deqiang He , Jiayang Zhao , Zhenzhen Jin , Chenggeng Huang , Cai Yi , Jinxin Wu","doi":"10.1016/j.ress.2025.110978","DOIUrl":"10.1016/j.ress.2025.110978","url":null,"abstract":"<div><div>Accurate prediction of Bearings' remaining useful life (RUL) is crucial in equipment operation and maintenance. The bearing RUL prediction technology based on GCN has recently been widely used. However, the existing GCN-based RUL prediction results are limited by two aspects : (1) GCN usually uses the predefined adjacency matrix to define the graph, which makes the graph unable to track the real-time correlation of degradation features in time. (2) Existing GCN uses only one to two layers of graph convolution and cannot extract deep features. Based on the issues above, this paper proposes a bearing RUL prediction model that utilizes a Dual-correlation adaptive gated graph convolutional network (DCAGGCN). Firstly, a predefined double correlation graph is proposed and obtained by feature channel data. Next, an adaptive graph is created by transforming a source matrix and a target matrix, and then integrating it with a predefined graph. This allows the network to consider two types of correlation and adaptively adjust the graph's topology. In addition, this paper proposes a gated convolution layer, which can greatly alleviate the over-smoothing problem caused by the stacking of graph convolution layers. The effectiveness of the proposed method is verified by two public datasets.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110978"},"PeriodicalIF":9.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578816","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 estimation of natural gas leakage source terms using physical information and improved particle filtering

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-01 DOI: 10.1016/j.ress.2025.110989

Qi Jing , Xingwang Song , Bingcai Sun , Yuntao Li , Laibin Zhang

Natural gas pipeline leaks can cause fires or explosions, making quick and accurate leak source identification critical for emergency response. This study develops a natural gas pipeline leakage source inversion model, where a Proper Orthogonal Decomposition-Physics-Informed Neural Network (POD-PINN) is integrated as the gas forward diffusion model. The inversion model combines an improved particle filtering algorithm, gas sensor data, and the POD-PINN, enabling rapid identification of leakage source terms. The gas source estimation results using POD-PINN and the Gaussian model as forward models were compared across different scenarios, and the impact of sensor errors on the inversion model was analyzed. Using POD-PINN as the forward model preserves accuracy while improving computational efficiency. The inclusion of a Gaussian kernel function and Markov Chain Monte Carlo (MCMC) method addresses degeneracy and impoverishment issues in standard particle filtering, preventing convergence to local optima. Results show that, across different scenarios, spatial position estimation errors are under 5%, and source strength errors are below 8%. When sensor measurement error is exceeds 0.5, the model cannot accurately estimate all source parameters. The proposed inversion model is subjected to convergence analysis, confirming its feasibility.

{"title":"Efficient estimation of natural gas leakage source terms using physical information and improved particle filtering","authors":"Qi Jing , Xingwang Song , Bingcai Sun , Yuntao Li , Laibin Zhang","doi":"10.1016/j.ress.2025.110989","DOIUrl":"10.1016/j.ress.2025.110989","url":null,"abstract":"<div><div>Natural gas pipeline leaks can cause fires or explosions, making quick and accurate leak source identification critical for emergency response. This study develops a natural gas pipeline leakage source inversion model, where a Proper Orthogonal Decomposition-Physics-Informed Neural Network (POD-PINN) is integrated as the gas forward diffusion model. The inversion model combines an improved particle filtering algorithm, gas sensor data, and the POD-PINN, enabling rapid identification of leakage source terms. The gas source estimation results using POD-PINN and the Gaussian model as forward models were compared across different scenarios, and the impact of sensor errors on the inversion model was analyzed. Using POD-PINN as the forward model preserves accuracy while improving computational efficiency. The inclusion of a Gaussian kernel function and Markov Chain Monte Carlo (MCMC) method addresses degeneracy and impoverishment issues in standard particle filtering, preventing convergence to local optima. Results show that, across different scenarios, spatial position estimation errors are under 5%, and source strength errors are below 8%. When sensor measurement error is exceeds 0.5, the model cannot accurately estimate all source parameters. The proposed inversion model is subjected to convergence analysis, confirming its feasibility.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110989"},"PeriodicalIF":9.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619490","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

GE-MBAT: An efficient algorithm for reliability assessment in multi-state flow networks

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-01 DOI: 10.1016/j.ress.2025.110916

Zhifeng Hao , Wei-Chang Yeh

Multi-state flow networks are increasingly critical across diverse applications such as network resilience, Internet of Things (IoT), and facility networks. These networks provide a more realistic representation of operational environments compared to binary-state models. Ensuring reliable network performance is crucial for the continuous and effective operation of these multi-state flow networks, especially as they grow in complexity. However, assessing reliability presents significant challenges due to the computational complexity involved. This paper introduces the "Greater than or Equal to" Multi-State Binary-Addition-Tree (GE-MBAT), designed to identify all vectors X of which (the maximum flow in the subgraph resulting from X) ≥ d rather than generating all possible multi-state vectors to enhance the efficiency and accuracy of reliability calculations in multi-state networks. The GE-MBAT reduces the generation of infeasible vectors, outperforming traditional methods in computational efficiency. This research contributes to the development of more reliable and robust network systems, with significant implications for critical infrastructure and advanced network technologies.

{"title":"GE-MBAT: An efficient algorithm for reliability assessment in multi-state flow networks","authors":"Zhifeng Hao , Wei-Chang Yeh","doi":"10.1016/j.ress.2025.110916","DOIUrl":"10.1016/j.ress.2025.110916","url":null,"abstract":"<div><div>Multi-state flow networks are increasingly critical across diverse applications such as network resilience, Internet of Things (IoT), and facility networks. These networks provide a more realistic representation of operational environments compared to binary-state models. Ensuring reliable network performance is crucial for the continuous and effective operation of these multi-state flow networks, especially as they grow in complexity. However, assessing reliability presents significant challenges due to the computational complexity involved. This paper introduces the \"Greater than or Equal to\" Multi-State Binary-Addition-Tree (GE-MBAT), designed to identify all vectors <em>X</em> of which (the maximum flow in the subgraph resulting from <em>X</em>) ≥ <em>d</em> rather than generating all possible multi-state vectors to enhance the efficiency and accuracy of reliability calculations in multi-state networks. The GE-MBAT reduces the generation of infeasible vectors, outperforming traditional methods in computational efficiency. This research contributes to the development of more reliable and robust network systems, with significant implications for critical infrastructure and advanced network technologies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110916"},"PeriodicalIF":9.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578811","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

Enhancing resilience of unmanned autonomous swarms through game theory-based cooperative reconfiguration

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-28 DOI: 10.1016/j.ress.2025.110951

Chengxing Wu , Hongzhong Deng , Hongqian Wu , Chengyi Tu

The resilience of unmanned autonomous swarms (UAS) is critical for their ability to adjust behaviors and maintain essential functions when errors and failures occur. While significant advancements have been made in enhancing UAS resilience, the potential to utilize their inherent self-organizing and self-restructuring capabilities for further improvement remains largely underexplored. In this study, we present a game theory-based reconfiguration framework for UAS, enabling dynamic adjustments to the swarm’s network structure through cooperative payoffs. Building on this framework, we propose a UAS resilience metric to quantify the swarm’s task performance under continuous disturbances, validated through a case study. Finally, our analysis of the optimal configurations for enhancing UAS resilience—considering payoff matrices, swarm composition, communication range, and network structure—provides actionable insights for UAS design. We find that an optimal agent configuration ratio exists that maximizes UAS resilience, with specific constraints established for this ratio. Additionally, while increasing the communication range improves resilience, the benefits diminish beyond a certain threshold. We also find that network topology significantly impacts UAS resilience, particularly in structures with short global paths, which exhibit greater resilience.

{"title":"Enhancing resilience of unmanned autonomous swarms through game theory-based cooperative reconfiguration","authors":"Chengxing Wu , Hongzhong Deng , Hongqian Wu , Chengyi Tu","doi":"10.1016/j.ress.2025.110951","DOIUrl":"10.1016/j.ress.2025.110951","url":null,"abstract":"<div><div>The resilience of unmanned autonomous swarms (UAS) is critical for their ability to adjust behaviors and maintain essential functions when errors and failures occur. While significant advancements have been made in enhancing UAS resilience, the potential to utilize their inherent self-organizing and self-restructuring capabilities for further improvement remains largely underexplored. In this study, we present a game theory-based reconfiguration framework for UAS, enabling dynamic adjustments to the swarm’s network structure through cooperative payoffs. Building on this framework, we propose a UAS resilience metric to quantify the swarm’s task performance under continuous disturbances, validated through a case study. Finally, our analysis of the optimal configurations for enhancing UAS resilience—considering payoff matrices, swarm composition, communication range, and network structure—provides actionable insights for UAS design. We find that an optimal agent configuration ratio exists that maximizes UAS resilience, with specific constraints established for this ratio. Additionally, while increasing the communication range improves resilience, the benefits diminish beyond a certain threshold. We also find that network topology significantly impacts UAS resilience, particularly in structures with short global paths, which exhibit greater resilience.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110951"},"PeriodicalIF":9.4,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578812","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 comparative assessment of domino accident analysis methods in process industries using LMAW and DNMA techniques

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-28 DOI: 10.1016/j.ress.2025.110981

Sarbast Moslem , Kamran Gholamizadeh , Esmaeil Zarei , Hans J Pasman , Beatriz Martinez-Pastor , Francesco Pilla

Investigating domino incidents in process industries is critical for enhancing safety and preventing cascading failures with potentially severe consequences. A review of existing accident investigative methods underscores the need for appropriate criteria and methodologies, ensuring comprehensive analysis, and thus effective prevention possibility. This study addresses this need by evaluating, comparing, and ranking the effectiveness of various investigative methods. Ranking techniques of alternatives are many and show a steady improvement trend. This research applies the latest: the Logarithmic Methodology of Additive Weights (LMAW) to assign importance weights to relevant criteria. It then utilizes the Double Normalization-Based Multiple Aggregation (DNMA) technique to evaluate and rank the methods. Based on comparisons and sensitivity analysis this dual approach ensures a robust and objective assessment of the methods used in accident analysis. The criteria of applicability, accuracy, and comprehensiveness were given the highest weights based on expert judgments. AcciMap emerged as the most effective among the methods assessed, demonstrating superior performance in various aspects of accident analysis, followed by CAST and FRAM. AcciMap achieved the top ranking, exhibiting the highest overall effectiveness. These findings offer guidance for selecting accident analysis methods, aiding managers and safety practitioners in process industries. By leveraging these insights, organizations can make informed decisions on the most suitable methods for investigating domino incidents, thereby improving safety measures and response strategies.

{"title":"A comparative assessment of domino accident analysis methods in process industries using LMAW and DNMA techniques","authors":"Sarbast Moslem , Kamran Gholamizadeh , Esmaeil Zarei , Hans J Pasman , Beatriz Martinez-Pastor , Francesco Pilla","doi":"10.1016/j.ress.2025.110981","DOIUrl":"10.1016/j.ress.2025.110981","url":null,"abstract":"<div><div>Investigating domino incidents in process industries is critical for enhancing safety and preventing cascading failures with potentially severe consequences. A review of existing accident investigative methods underscores the need for appropriate criteria and methodologies, ensuring comprehensive analysis, and thus effective prevention possibility. This study addresses this need by evaluating, comparing, and ranking the effectiveness of various investigative methods. Ranking techniques of alternatives are many and show a steady improvement trend. This research applies the latest: the Logarithmic Methodology of Additive Weights (LMAW) to assign importance weights to relevant criteria. It then utilizes the Double Normalization-Based Multiple Aggregation (DNMA) technique to evaluate and rank the methods. Based on comparisons and sensitivity analysis this dual approach ensures a robust and objective assessment of the methods used in accident analysis. The criteria of applicability, accuracy, and comprehensiveness were given the highest weights based on expert judgments. AcciMap emerged as the most effective among the methods assessed, demonstrating superior performance in various aspects of accident analysis, followed by CAST and FRAM. AcciMap achieved the top ranking, exhibiting the highest overall effectiveness. These findings offer guidance for selecting accident analysis methods, aiding managers and safety practitioners in process industries. By leveraging these insights, organizations can make informed decisions on the most suitable methods for investigating domino incidents, thereby improving safety measures and response strategies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110981"},"PeriodicalIF":9.4,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578814","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

Prescribing optimal health-aware operation for urban air mobility with deep reinforcement learning

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-27 DOI: 10.1016/j.ress.2025.110897

Mina Montazeri , Chetan S. Kulkarni , Olga Fink

Urban Air Mobility (UAM) aims to expand existing transportation networks in metropolitan areas by offering short flights either to transport passengers or cargo. Electric vertical takeoff and landing aircraft powered by lithium-ion battery packs are considered promising for such applications. Efficient mission planning is crucial, maximizing the number of flights per battery charge while ensuring completion even under unforeseen events. As batteries degrade, precise mission planning becomes challenging due to uncertainties in the end-of-discharge prediction. This often leads to adding safety margins, reducing the number or duration of potential flights on one battery charge. While predicting the end of discharge can support decision-making, it remains insufficient in case of unforeseen events, such as adverse weather conditions. This necessitates health-aware real-time control to address any unexpected events and extend the time until the end of charge while taking the current degradation state into account. This paper addresses the joint problem of mission planning and health-aware real-time control of operational parameters to prescriptively control the duration of one discharge cycle of the battery pack. We propose an algorithm that proactively prescribes operational parameters to extend the discharge cycle based on the battery’s current health status while optimizing the mission. The proposed deep reinforcement learning algorithm facilitates operational parameter optimization and path planning while accounting for the degradation state, even in the presence of uncertainties. Evaluation of simulated flights of a National Aeronautics and Space Administration (NASA) conceptual multirotor aircraft model, collected from Hardware-in-the-loop experiments, demonstrates the algorithm’s near-optimal performance across various operational scenarios, allowing adaptation to changed environmental conditions. The proposed health-aware prescriptive algorithm enables a more flexible and efficient operation not only in single aircraft but also in fleet operations, increasing the overall system throughput.

{"title":"Prescribing optimal health-aware operation for urban air mobility with deep reinforcement learning","authors":"Mina Montazeri , Chetan S. Kulkarni , Olga Fink","doi":"10.1016/j.ress.2025.110897","DOIUrl":"10.1016/j.ress.2025.110897","url":null,"abstract":"<div><div>Urban Air Mobility (UAM) aims to expand existing transportation networks in metropolitan areas by offering short flights either to transport passengers or cargo. Electric vertical takeoff and landing aircraft powered by lithium-ion battery packs are considered promising for such applications. Efficient mission planning is crucial, maximizing the number of flights per battery charge while ensuring completion even under unforeseen events. As batteries degrade, precise mission planning becomes challenging due to uncertainties in the end-of-discharge prediction. This often leads to adding safety margins, reducing the number or duration of potential flights on one battery charge. While predicting the end of discharge can support decision-making, it remains insufficient in case of unforeseen events, such as adverse weather conditions. This necessitates health-aware real-time control to address any unexpected events and extend the time until the end of charge while taking the current degradation state into account. This paper addresses the joint problem of mission planning and health-aware real-time control of operational parameters to prescriptively control the duration of one discharge cycle of the battery pack. We propose an algorithm that proactively prescribes operational parameters to extend the discharge cycle based on the battery’s current health status while optimizing the mission. The proposed deep reinforcement learning algorithm facilitates operational parameter optimization and path planning while accounting for the degradation state, even in the presence of uncertainties. Evaluation of simulated flights of a National Aeronautics and Space Administration (NASA) conceptual multirotor aircraft model, collected from Hardware-in-the-loop experiments, demonstrates the algorithm’s near-optimal performance across various operational scenarios, allowing adaptation to changed environmental conditions. The proposed health-aware prescriptive algorithm enables a more flexible and efficient operation not only in single aircraft but also in fleet operations, increasing the overall system throughput.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110897"},"PeriodicalIF":9.4,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511315","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

A reliability-based approach to identify critical components in a UHVDC converter station system against earthquakes

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-27 DOI: 10.1016/j.ress.2025.110977

Huangbin Liang

Earthquakes pose a huge threat to the power system in seismically active regions. Ultra-High Voltage Direct Current (UHVDC) converter stations become integral to modern power grids, especially for long-distance power transmission, and thus understanding and improving their seismic reliability is essential for ensuring the robustness of the power system. This paper presents a comprehensive reliability-based approach to identify critical components within UHVDC converter stations, focusing on seismic reliability. A seismic reliability index is defined as the expected post-earthquake transmission capacity loss, considering both the earthquake probability and the derated capacity under different operation modes. The converter system's seismic reliability model is established based on divide-and-group principles, dividing it into subsystems and deriving an equivalent logical model based on their interdependency. Failure probabilities of subsystems, consisting of wire-interconnected electrical equipment, are determined through finite element models and seismic vulnerability analysis, accounting for wire interaction forces. Advanced sensitivity analysis techniques such as the Morris method and Sobol's analysis identify critical components influencing seismic reliability. A case study on a real-world ±800 kV UHVDC converter station system demonstrates the effectiveness of the proposed approach in enhancing seismic reliability efficiently.

{"title":"A reliability-based approach to identify critical components in a UHVDC converter station system against earthquakes","authors":"Huangbin Liang","doi":"10.1016/j.ress.2025.110977","DOIUrl":"10.1016/j.ress.2025.110977","url":null,"abstract":"<div><div>Earthquakes pose a huge threat to the power system in seismically active regions. Ultra-High Voltage Direct Current (UHVDC) converter stations become integral to modern power grids, especially for long-distance power transmission, and thus understanding and improving their seismic reliability is essential for ensuring the robustness of the power system. This paper presents a comprehensive reliability-based approach to identify critical components within UHVDC converter stations, focusing on seismic reliability. A seismic reliability index is defined as the expected post-earthquake transmission capacity loss, considering both the earthquake probability and the derated capacity under different operation modes. The converter system's seismic reliability model is established based on divide-and-group principles, dividing it into subsystems and deriving an equivalent logical model based on their interdependency. Failure probabilities of subsystems, consisting of wire-interconnected electrical equipment, are determined through finite element models and seismic vulnerability analysis, accounting for wire interaction forces. Advanced sensitivity analysis techniques such as the Morris method and Sobol's analysis identify critical components influencing seismic reliability. A case study on a real-world ±800 kV UHVDC converter station system demonstrates the effectiveness of the proposed approach in enhancing seismic reliability efficiently.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110977"},"PeriodicalIF":9.4,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547941","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 novel model and simulation method for multivariate Gaussian fields involving nonlinear probabilistic dependencies and different variable-wise spatial variabilities

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-27 DOI: 10.1016/j.ress.2025.110963

Meng-Ze Lyu , Yang-Yi Liu , Jian-Bing Chen

The inherent randomness of engineering structures significantly influences the analysis of structural stochastic responses and safety assessments. It is critical to quantify the three aspects of random fields, including the randomness of individual variables, the probabilistic interdependence among multiple variables, and the spatiotemporal correlation of fields. This paper introduces a novel modeling framework for multivariate fields that accommodates both nonlinear probabilistic dependencies captured through copula, and the distinct spatial variability of individual fields described by correlation functions. Specifically, the framework defines a new analytical function, termed the bridge function, which establishes the relationship between the correlation functions of two fields governed by any copula structure. This proves the consistency of the new model, i.e., the copula function, as a between-variable constraint, allows the spatial correlation function of different variables to be freely selected, either with different correlation length or even with different shape. Further, to facilitate simulation, by the bridge function samples from multiple independent Gaussian fields can be onverted into those of multivariate fields that involve the specified vine copula dependencies and individual correlation functions. This approach addresses the challenge of simultaneously satisfying nonlinear dependencies and spatial variability in multivariate field simulations. The paper details the analytical expressions and numerical solution procedures for the bridge function, along with a comprehensive simulation method that integrates vine-copula-based conditional sampling and stochastic harmonic functions. The effectiveness of the proposed method is validated through various engineering application case studies, demonstrating its potential for accurate uncertainty quantification in complex engineering scenarios.

{"title":"A novel model and simulation method for multivariate Gaussian fields involving nonlinear probabilistic dependencies and different variable-wise spatial variabilities","authors":"Meng-Ze Lyu , Yang-Yi Liu , Jian-Bing Chen","doi":"10.1016/j.ress.2025.110963","DOIUrl":"10.1016/j.ress.2025.110963","url":null,"abstract":"<div><div>The inherent randomness of engineering structures significantly influences the analysis of structural stochastic responses and safety assessments. It is critical to quantify the three aspects of random fields, including the randomness of individual variables, the probabilistic interdependence among multiple variables, and the spatiotemporal correlation of fields. This paper introduces a novel modeling framework for multivariate fields that accommodates both nonlinear probabilistic dependencies captured through copula, and the distinct spatial variability of individual fields described by correlation functions. Specifically, the framework defines a new analytical function, termed the bridge function, which establishes the relationship between the correlation functions of two fields governed by any copula structure. This proves the consistency of the new model, i.e., the copula function, as a between-variable constraint, allows the spatial correlation function of different variables to be freely selected, either with different correlation length or even with different shape. Further, to facilitate simulation, by the bridge function samples from multiple independent Gaussian fields can be onverted into those of multivariate fields that involve the specified vine copula dependencies and individual correlation functions. This approach addresses the challenge of simultaneously satisfying nonlinear dependencies and spatial variability in multivariate field simulations. The paper details the analytical expressions and numerical solution procedures for the bridge function, along with a comprehensive simulation method that integrates vine-copula-based conditional sampling and stochastic harmonic functions. The effectiveness of the proposed method is validated through various engineering application case studies, demonstrating its potential for accurate uncertainty quantification in complex engineering scenarios.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110963"},"PeriodicalIF":9.4,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547946","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