Reliability Engineering & System Safety最新文献

英文中文

Physics-based pruning neural network for global sensitivity analysis

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-16 DOI: 10.1016/j.ress.2025.110925

Zhiwei Bai , Shufang Song

Global sensitivity analysis (GSA) is essential to quantify the variation of model response sourced from the uncertainty of input variables over the entire design space. To address challenges in high-dimensional complex problems with dependent variables, a novel physics-based pruning neural network (PbPNN) approach is proposed. The PbPNN innovatively performs network pruning based on the properties of unconditional and conditional variances. Through the mask matrix of specific settings, a pruning neural network with 3-dimensional outputs (an unconditional and two conditional responses) is constructed. The PbPNN method not only simultaneously calculates the unconditional and conditional variances but also effectively identifies the contributions from variable dependencies and interactions. Furthermore, the PbPNN method remains unaffected by the dimensionality of the problem, making it well-suited for high-dimensional complex problems. The effectiveness and accuracy of the proposed method are demonstrated through three numerical examples, where the PbPNN outperformed traditional methods in both sensitivity quantification and computational efficiency. Two engineering examples further validate the method's potential, proving the value of combining machine learning with the properties of unconditional and conditional variances in GSA.

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引用次数: 0

Rolling bearing degradation stage division and RUL prediction based on recursive exponential slow feature analysis and Bi-LSTM model

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-16 DOI: 10.1016/j.ress.2025.110923

Xinliang Li , Wan Zhang , Yu Ding , Jun Cai , Xiaoan Yan

Accurately predicting the remaining useful life (RUL) of rolling bearings is essential for effective system health management and maintenance in mechanical systems. Traditional RUL prediction methods often suffer from susceptibility to noise, leading to instability in feature extraction and inadequate capture of long-term change trends. To address this challenge, this paper proposes a rolling bearing RUL prediction method based on recursive exponential slow feature analysis (RESFA) and bidirectional long short-term memory (Bi-LSTM) network. Initially, the vibration signal is input into a convolutional neural network for health state classification, and the "3/5" principle is applied to determine the degradation starting (DS) point. Subsequently, features are extracted based on an autoencoder. Additionally, RESFA is utilized to extract long-term degradation trends within the system. Finally, the features extracted from the autoencoder and the slow feature are integrated, and the fused features are inputted into a Bi-LSTM model for accurate bearing RUL prediction. The efficacy of the proposed approach is validated using datasets from the IEEE PHM Prognostic Challenge, the XJTU-SY and ABLT-1A dataests. The prediction accuracy of the method proposed in this paper exceeds that of other state-of-the-art methods, highlighting the effectiveness of the RESFA-based approach in the field of rolling bearing RUL prediction.

{"title":"Rolling bearing degradation stage division and RUL prediction based on recursive exponential slow feature analysis and Bi-LSTM model","authors":"Xinliang Li , Wan Zhang , Yu Ding , Jun Cai , Xiaoan Yan","doi":"10.1016/j.ress.2025.110923","DOIUrl":"10.1016/j.ress.2025.110923","url":null,"abstract":"<div><div>Accurately predicting the remaining useful life (RUL) of rolling bearings is essential for effective system health management and maintenance in mechanical systems. Traditional RUL prediction methods often suffer from susceptibility to noise, leading to instability in feature extraction and inadequate capture of long-term change trends. To address this challenge, this paper proposes a rolling bearing RUL prediction method based on recursive exponential slow feature analysis (RESFA) and bidirectional long short-term memory (Bi-LSTM) network. Initially, the vibration signal is input into a convolutional neural network for health state classification, and the \"3/5\" principle is applied to determine the degradation starting (DS) point. Subsequently, features are extracted based on an autoencoder. Additionally, RESFA is utilized to extract long-term degradation trends within the system. Finally, the features extracted from the autoencoder and the slow feature are integrated, and the fused features are inputted into a Bi-LSTM model for accurate bearing RUL prediction. The efficacy of the proposed approach is validated using datasets from the IEEE PHM Prognostic Challenge, the XJTU-SY and ABLT-1A dataests. The prediction accuracy of the method proposed in this paper exceeds that of other state-of-the-art methods, highlighting the effectiveness of the RESFA-based approach in the field of rolling bearing RUL prediction.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110923"},"PeriodicalIF":9.4,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510496","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

Corrigendum to “A Condition-Based Maintenance Policy for Continuously Monitored Multi-Component Systems with Economic and Stochastic Dependence” [Reliability Engineering & System Safety 222 (2022) 108321]

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-15 DOI: 10.1016/j.ress.2025.110874

Jordan L. Oakley, Kevin J. Wilson, Pete Philipson

引用次数: 0

A fire navigation model: Considering travel time, impact of fire, and congestion severity

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-15 DOI: 10.1016/j.ress.2025.110914

Feze Golshani , Liping Fang

The navigation model presented integrates Fire Dynamics Simulation (FDS), a navigation graph generation model, a modified Dijkstra algorithm, Agent-Based Simulation (ABS), and Intelligent Active Dynamic Signage System (IADSS). The FDS evaluates fire impacts on paths’ safety, while ABS captures evacuees’ interactions and congestion. The modified Dijkstra algorithm identifies optimal paths, considering travel time, fire impacts, and crowd density. The IADSS dynamically communicates these paths to evacuees. The contributions include (1) integrating the combined effects of heat and toxic gases on evacuees with congestion and travel time into an evacuation framework, (2) introducing algorithms for integrating signage systems into buildings’ navigation graph, (3) determining recommended and negated signage directions, (4) proposing a central server that updates the signage directions concerning real-time buildings’ conditions and congestion, and (5) developing a tool for minimizing evacuation time, congestion, and fire impacts. Case studies across diverse fire scenarios and building types showcase the framework's adaptability. Results indicate significant improvements over traditional methods, including reduced evacuation time, congestion severity, and cumulative fire impacts. Furthermore, the model computational efficiency enables time-sensitive fire evacuation planning. A validation study comparing it with two established methodologies highlights its superior signage direction determination, heightened evacuation performance, and enhanced output richness.

{"title":"A fire navigation model: Considering travel time, impact of fire, and congestion severity","authors":"Feze Golshani , Liping Fang","doi":"10.1016/j.ress.2025.110914","DOIUrl":"10.1016/j.ress.2025.110914","url":null,"abstract":"<div><div>The navigation model presented integrates Fire Dynamics Simulation (FDS), a navigation graph generation model, a modified Dijkstra algorithm, Agent-Based Simulation (ABS), and Intelligent Active Dynamic Signage System (IADSS). The FDS evaluates fire impacts on paths’ safety, while ABS captures evacuees’ interactions and congestion. The modified Dijkstra algorithm identifies optimal paths, considering travel time, fire impacts, and crowd density. The IADSS dynamically communicates these paths to evacuees. The contributions include (1) integrating the combined effects of heat and toxic gases on evacuees with congestion and travel time into an evacuation framework, (2) introducing algorithms for integrating signage systems into buildings’ navigation graph, (3) determining recommended and negated signage directions, (4) proposing a central server that updates the signage directions concerning real-time buildings’ conditions and congestion, and (5) developing a tool for minimizing evacuation time, congestion, and fire impacts. Case studies across diverse fire scenarios and building types showcase the framework's adaptability. Results indicate significant improvements over traditional methods, including reduced evacuation time, congestion severity, and cumulative fire impacts. Furthermore, the model computational efficiency enables time-sensitive fire evacuation planning. A validation study comparing it with two established methodologies highlights its superior signage direction determination, heightened evacuation performance, and enhanced output richness.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110914"},"PeriodicalIF":9.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510495","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

Identifying critical road segments and optimizing resilience strategies based on multi-state congested characteristics

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-15 DOI: 10.1016/j.ress.2025.110912

Xiushi Dong , Hongjun Cui , Yue Su , Minqing Zhu , Sheng Yao

The urban transportation systems are susceptible to congestion, accidents, and other costly delays, and recovery is challenging. Accurately identifying critical road segments and determining the optimization order are essential prerequisites for significantly improving network resilience. In this context, this paper proposes a method for identifying critical segments in congested road networks and a resilience optimization strategy based on the optimized order. Firstly, multiple congested states of segments are classified by their speed, and the performance is estimated by the probability-based method. Then, performance and temporal dimensions are combined to evaluate segment resilience. After that, the concept of local network resilience for segments is introduced. A resilience-based method for identifying critical road segments is proposed considering both segment performance and their connections with the network. Finally, the proposed resilience optimization strategy is demonstrated to significantly enhance network resilience by comparing it based only on complex network approaches or segment resilience. This has practical implications for alleviating traffic congestion or determining post-disaster network repair priorities.

{"title":"Identifying critical road segments and optimizing resilience strategies based on multi-state congested characteristics","authors":"Xiushi Dong , Hongjun Cui , Yue Su , Minqing Zhu , Sheng Yao","doi":"10.1016/j.ress.2025.110912","DOIUrl":"10.1016/j.ress.2025.110912","url":null,"abstract":"<div><div>The urban transportation systems are susceptible to congestion, accidents, and other costly delays, and recovery is challenging. Accurately identifying critical road segments and determining the optimization order are essential prerequisites for significantly improving network resilience. In this context, this paper proposes a method for identifying critical segments in congested road networks and a resilience optimization strategy based on the optimized order. Firstly, multiple congested states of segments are classified by their speed, and the performance is estimated by the probability-based method. Then, performance and temporal dimensions are combined to evaluate segment resilience. After that, the concept of local network resilience for segments is introduced. A resilience-based method for identifying critical road segments is proposed considering both segment performance and their connections with the network. Finally, the proposed resilience optimization strategy is demonstrated to significantly enhance network resilience by comparing it based only on complex network approaches or segment resilience. This has practical implications for alleviating traffic congestion or determining post-disaster network repair priorities.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"258 ","pages":"Article 110912"},"PeriodicalIF":9.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437027","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

Railway operational hazard prediction and control based on knowledge graph embedding and topological analysis

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-15 DOI: 10.1016/j.ress.2025.110917

Jintao Liu , Lin Ji , Keyi Chen , Chenling Li , Huayu Duan

Railway operational accidents usually result from the domino effects of a series of interrelated hazards. Predicting and controlling potential hazards in advance are valuable for ensuring safe railway operations. A variety of hazards form a heterogeneous hazard relationship network because of their complex interactions. The potential hazards can be predicted and controlled by use of such a relationship network structure. In this paper, a new knowledge graph-based hazard prediction and control approach is proposed, aiming to prevent railway operational accidents using the relationship network of hazards. Its originality is to leverage knowledge graph embedding and topological analysis to predict and control hazards, by means of both a novel convolutional architecture on hyperplanes and some tailored topological indicators. The outcomes of the proposed approach can offer railway operators the decision basis of accident prevention, in the form of potential hazards and their corresponding control measures. An application to the UK's railway accident data shows that 13.25 % and 4.38 % of hazard prediction accuracy gains in Hit@3 and Hit@10 evaluation metrics are respectively achieved by the proposed method over the best baseline methods. Furthermore, it also demonstrates the effectiveness of the proposed method in formulating targeted hazard control measures.

{"title":"Railway operational hazard prediction and control based on knowledge graph embedding and topological analysis","authors":"Jintao Liu , Lin Ji , Keyi Chen , Chenling Li , Huayu Duan","doi":"10.1016/j.ress.2025.110917","DOIUrl":"10.1016/j.ress.2025.110917","url":null,"abstract":"<div><div>Railway operational accidents usually result from the domino effects of a series of interrelated hazards. Predicting and controlling potential hazards in advance are valuable for ensuring safe railway operations. A variety of hazards form a heterogeneous hazard relationship network because of their complex interactions. The potential hazards can be predicted and controlled by use of such a relationship network structure. In this paper, a new knowledge graph-based hazard prediction and control approach is proposed, aiming to prevent railway operational accidents using the relationship network of hazards. Its originality is to leverage knowledge graph embedding and topological analysis to predict and control hazards, by means of both a novel convolutional architecture on hyperplanes and some tailored topological indicators. The outcomes of the proposed approach can offer railway operators the decision basis of accident prevention, in the form of potential hazards and their corresponding control measures. An application to the UK's railway accident data shows that 13.25 % and 4.38 % of hazard prediction accuracy gains in Hit@3 and Hit@10 evaluation metrics are respectively achieved by the proposed method over the best baseline methods. Furthermore, it also demonstrates the effectiveness of the proposed method in formulating targeted hazard control measures.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"258 ","pages":"Article 110917"},"PeriodicalIF":9.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445435","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 systems-theoretic approach using association rule mining and predictive Bayesian trend analysis to identify patterns in maritime accident causes

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-15 DOI: 10.1016/j.ress.2025.110911

Shahrokh Bairami-Khankandi , Victor Bolbot , Ahmad BahooToroody , Floris Goerlandt

Accident investigations are commonly conducted to improve safety in ship design and operations. Given the lack of comprehensive approaches to understand causal factors of maritime accidents considering systems-theoretic views on accident causation, this paper presents a novel approach using information from accident investigation reports to this effect. The proposed approach combines key elements of the Causal Analysis based on Systems Theory method, Association Rule Mining and predictive Bayesian trend analysis to gain deeper understanding of patterns and trends in accident causal factors. This new approach goes beyond the state of the art by offering insights on accident causal patterns and trends at the system level, which can be used by maritime authorities and industries to enhance maritime safety by understanding co-occurring accident causes. Additionally, the approach is applied to 30 years of Canadian shipping accident reports from the Transportation Safety Board, producing new knowledge about accident causes across different commercial vessel types and accident categories. The results highlight accident causes in interactions between shipping management and vessels, and between ship crews and bridge equipment. Differences between passenger and cargo vessels, and between onboard fires and navigational accidents are observed. Discussions on results, limitations, and future research directions conclude the article.

{"title":"A systems-theoretic approach using association rule mining and predictive Bayesian trend analysis to identify patterns in maritime accident causes","authors":"Shahrokh Bairami-Khankandi , Victor Bolbot , Ahmad BahooToroody , Floris Goerlandt","doi":"10.1016/j.ress.2025.110911","DOIUrl":"10.1016/j.ress.2025.110911","url":null,"abstract":"<div><div>Accident investigations are commonly conducted to improve safety in ship design and operations. Given the lack of comprehensive approaches to understand causal factors of maritime accidents considering systems-theoretic views on accident causation, this paper presents a novel approach using information from accident investigation reports to this effect. The proposed approach combines key elements of the Causal Analysis based on Systems Theory method, Association Rule Mining and predictive Bayesian trend analysis to gain deeper understanding of patterns and trends in accident causal factors. This new approach goes beyond the state of the art by offering insights on accident causal patterns and trends at the system level, which can be used by maritime authorities and industries to enhance maritime safety by understanding co-occurring accident causes. Additionally, the approach is applied to 30 years of Canadian shipping accident reports from the Transportation Safety Board, producing new knowledge about accident causes across different commercial vessel types and accident categories. The results highlight accident causes in interactions between shipping management and vessels, and between ship crews and bridge equipment. Differences between passenger and cargo vessels, and between onboard fires and navigational accidents are observed. Discussions on results, limitations, and future research directions conclude the article.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"258 ","pages":"Article 110911"},"PeriodicalIF":9.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453546","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

Remaining useful life prediction method based on two-phase adaptive drift Wiener process

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-15 DOI: 10.1016/j.ress.2025.110908

Zhijian Wang , Pengwei Jiang , Zhongxin Chen , Yanfeng Li , Weibo Ren , Lei Dong , Wenhua Du , Junyuan Wang , Xiaohong Zhang , Hui Shi

The degradation process of components often shows as two-phase in reality, and the two-phase Wiener process has been widely used to model component degradation. However, previous studies have always assumed that the drift coefficient of each phase is constant, failing to capture the effects of external variations, which reduces the predictive performance of model. Thus, this paper establishes a two-phase adaptive drift Wiener process model to characterize the degradation of components. First, a phasing method is proposed that adaptively identifies the change point and uses fitting metrics to analyze determine if the point is anomalous data. Additionally, the adaptive drift method is innovatively introduced into the developed two-phase Wiener process model for updates. Then, the approximate analytical expression of the probability density function of the remaining useful life is derived and extended to the cases where uncertainty in the state at the change point and heterogeneity are considered. Finally, the feasibility of the proposed method is validated through numerical simulation and actual examples in the laboratory.

{"title":"Remaining useful life prediction method based on two-phase adaptive drift Wiener process","authors":"Zhijian Wang , Pengwei Jiang , Zhongxin Chen , Yanfeng Li , Weibo Ren , Lei Dong , Wenhua Du , Junyuan Wang , Xiaohong Zhang , Hui Shi","doi":"10.1016/j.ress.2025.110908","DOIUrl":"10.1016/j.ress.2025.110908","url":null,"abstract":"<div><div>The degradation process of components often shows as two-phase in reality, and the two-phase Wiener process has been widely used to model component degradation. However, previous studies have always assumed that the drift coefficient of each phase is constant, failing to capture the effects of external variations, which reduces the predictive performance of model. Thus, this paper establishes a two-phase adaptive drift Wiener process model to characterize the degradation of components. First, a phasing method is proposed that adaptively identifies the change point and uses fitting metrics to analyze determine if the point is anomalous data. Additionally, the adaptive drift method is innovatively introduced into the developed two-phase Wiener process model for updates. Then, the approximate analytical expression of the probability density function of the remaining useful life is derived and extended to the cases where uncertainty in the state at the change point and heterogeneity are considered. Finally, the feasibility of the proposed method is validated through numerical simulation and actual examples in the laboratory.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"258 ","pages":"Article 110908"},"PeriodicalIF":9.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429645","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

Computational fluid dynamics -informed virtual safety assessment of steel-framed structure with fire-induced ductile failure

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-15 DOI: 10.1016/j.ress.2025.110918

Zhiyi Shi , Yuan Feng , Temitope Egbelakin , Chengwei Yang , Wei Gao

This paper proposes a Computational Fluid Dynamics-Informed (CI) Virtual Safety Assessment (VSA) framework for predicting the time-dependent ductile failure of steel-framed buildings during fire incidents. By incorporating a CI-based physical model, the spatiotemporally nonlinear temperature field in real fire scenarios can be reproduced and used as thermal boundary conditions for sequential thermal-elastoplastic analysis, enabling the assessment of fire-induced structural responses. Additionally, non-deterministic material properties caused by manufacturing imperfections are considered to analyze their impacts on uncertain high-temperature structural ductile deformation. To achieve rapid assessment, a Virtual Modeling (VM) technique is introduced to capture the nonlinear relationship between physical input parameters and corresponding structural responses. The proposed CI-VSA framework is applied to two real steel structures, a steel-framed factory and a transmission tower, to verify its efficiency and accuracy. The results demonstrate that, compared to traditional simulation-based prediction methods, the proposed CI-VSA framework reduces computational resource consumption by 99% and achieves highly accurate predictions for most sample points, with relative errors below 1%, under a training sample size of 1,000. In practice, the CI-VSA framework enables continuous prediction of spatiotemporal structural responses through the analysis of fire-thermal-structural interactions, achieves real-time updates of structural safety statuses, and ultimately provides early-stage safety warnings.

{"title":"Computational fluid dynamics -informed virtual safety assessment of steel-framed structure with fire-induced ductile failure","authors":"Zhiyi Shi , Yuan Feng , Temitope Egbelakin , Chengwei Yang , Wei Gao","doi":"10.1016/j.ress.2025.110918","DOIUrl":"10.1016/j.ress.2025.110918","url":null,"abstract":"<div><div>This paper proposes a Computational Fluid Dynamics-Informed (CI) Virtual Safety Assessment (VSA) framework for predicting the time-dependent ductile failure of steel-framed buildings during fire incidents. By incorporating a CI-based physical model, the spatiotemporally nonlinear temperature field in real fire scenarios can be reproduced and used as thermal boundary conditions for sequential thermal-elastoplastic analysis, enabling the assessment of fire-induced structural responses. Additionally, non-deterministic material properties caused by manufacturing imperfections are considered to analyze their impacts on uncertain high-temperature structural ductile deformation. To achieve rapid assessment, a Virtual Modeling (VM) technique is introduced to capture the nonlinear relationship between physical input parameters and corresponding structural responses. The proposed CI-VSA framework is applied to two real steel structures, a steel-framed factory and a transmission tower, to verify its efficiency and accuracy. The results demonstrate that, compared to traditional simulation-based prediction methods, the proposed CI-VSA framework reduces computational resource consumption by 99% and achieves highly accurate predictions for most sample points, with relative errors below 1%, under a training sample size of 1,000. In practice, the CI-VSA framework enables continuous prediction of spatiotemporal structural responses through the analysis of fire-thermal-structural interactions, achieves real-time updates of structural safety statuses, and ultimately provides early-stage safety warnings.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"258 ","pages":"Article 110918"},"PeriodicalIF":9.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437025","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

Integrated production, maintenance and quality control for complex manufacturing systems considering imperfect maintenance and dynamic inspection

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-15 DOI: 10.1016/j.ress.2025.110896

Xiaotong Wei , Yalong Wang , Yingdong He , Zixian Liu , Zhen He

In complex manufacturing systems, production equipment typically consists of numerous components. Such equipment is prone to high failure rates in the early stages of use owing to a combination of manufacturing defects, design defects, and improper initial setup. In formulating production, maintenance, and quality policies, most joint optimization models consider that equipment failure rates gradually increase with use time. However, they ignore failures that might occur in the early stages of equipment use, resulting in high quality costs. Considering manufacturing defects in the early use of complex manufacturing systems, this study formulates a new joint optimization model that aims to determine the safety stock level, production cycle length, preventive maintenance threshold, and inspection sampling ratio that minimize the expected unit cost of the system. First, we consider the equipment failure rate with a bathtub curve shape and develop a corresponding dynamic sampling strategy. Second, we divide the production process into six scenarios and develop a condition-based maintenance policy based on the degree of equipment deterioration, production time, and average output quality limits. We solve the model using Monte Carlo simulation and design of experiments. Sensitivity analysis and comparative study verify that the proposed strategy can flexibly adapt to production changes with fewer inspections and lower costs.

{"title":"Integrated production, maintenance and quality control for complex manufacturing systems considering imperfect maintenance and dynamic inspection","authors":"Xiaotong Wei , Yalong Wang , Yingdong He , Zixian Liu , Zhen He","doi":"10.1016/j.ress.2025.110896","DOIUrl":"10.1016/j.ress.2025.110896","url":null,"abstract":"<div><div>In complex manufacturing systems, production equipment typically consists of numerous components. Such equipment is prone to high failure rates in the early stages of use owing to a combination of manufacturing defects, design defects, and improper initial setup. In formulating production, maintenance, and quality policies, most joint optimization models consider that equipment failure rates gradually increase with use time. However, they ignore failures that might occur in the early stages of equipment use, resulting in high quality costs. Considering manufacturing defects in the early use of complex manufacturing systems, this study formulates a new joint optimization model that aims to determine the safety stock level, production cycle length, preventive maintenance threshold, and inspection sampling ratio that minimize the expected unit cost of the system. First, we consider the equipment failure rate with a bathtub curve shape and develop a corresponding dynamic sampling strategy. Second, we divide the production process into six scenarios and develop a condition-based maintenance policy based on the degree of equipment deterioration, production time, and average output quality limits. We solve the model using Monte Carlo simulation and design of experiments. Sensitivity analysis and comparative study verify that the proposed strategy can flexibly adapt to production changes with fewer inspections and lower costs.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110896"},"PeriodicalIF":9.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474336","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

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