Reliability Engineering & System Safety最新文献_第7页

Rapid uncertainty quantification for structural full-field dynamic responses with extremely high dimension 超高维结构全场动力响应的快速不确定度量化

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-05 DOI: 10.1016/j.ress.2025.111097

Yue Zhao , Jie Liu , Yafeng Ren

{"title":"Rapid uncertainty quantification for structural full-field dynamic responses with extremely high dimension","authors":"Yue Zhao , Jie Liu , Yafeng Ren","doi":"10.1016/j.ress.2025.111097","DOIUrl":"10.1016/j.ress.2025.111097","url":null,"abstract":"<div><div>Conducting a full-field dynamic analysis under structural uncertainties is of great significance for a better understanding of structural mechanics behavior and obtaining structural reliability. However, the full-field dynamic response of structures is of extremely high dimensionality, and traditional uncertainty quantification (UQ) methods may face challenges such as modeling difficulties and low analysis efficiency. To address these issues, this paper proposes a rapid UQ analysis method for the structural full-field responses with extremely high dimension. This method first decouples the ultra-high dimensional full-field response based on modal analysis and further extracts features of the responses based on manifold learning techniques, effectively reducing the dimensionality of the response to be analyzed. Subsequently, by introducing the optimal sparse polynomial chaos expansion technique, an efficient UQ analysis model from structural uncertainty parameters to response is constructed. Three numerical examples are provided to demonstrate the accuracy of the proposed method. Throughout the entire UQ analysis process, only a small amount of low-dimensional features need to be analyzed, and the final UQ accuracy of the full-field dynamic response can be effectively guaranteed. Therefore, the proposed method provides an effective tool for rapid UQ analysis of structural full-field dynamic responses.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111097"},"PeriodicalIF":9.4,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806908","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

Graph-based reliability evaluation of a reconfigurable multi-stage system using sequential unconnected path sets 时序不连通路径集可重构多级系统的基于图的可靠性评估

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-04 DOI: 10.1016/j.ress.2025.111093

Lechang Yang , Jinwei Wang , Min Xie

{"title":"Graph-based reliability evaluation of a reconfigurable multi-stage system using sequential unconnected path sets","authors":"Lechang Yang , Jinwei Wang , Min Xie","doi":"10.1016/j.ress.2025.111093","DOIUrl":"10.1016/j.ress.2025.111093","url":null,"abstract":"<div><div>Admitting its potential in flexible manufacturing, the reconfigurable multi-stage system (RMS) is widely used in modern industries while its reliability is of great importance since the failure of any composing stage will lead to the system failure and abortion of the whole mission. In this paper, we present a survival signature-based framework for the reliability of an RMS. The idea of our approach is to convert a conventional probability estimation problem to a graph-based path-searching problem, thus the tedious Monte Carlo sampling is simplified. To this end, an unconnected path graph method is developed to calculate the number of working paths of the equivalent graph model of RMS. Instead of directly enumerating all possible working paths, those paths of interest are identified by searching unconnected nodes via backtracking while the computation cost is reduced. To further address the case of an RMS with shared components, a sequential unconnected path graph (SUPG) method is developed. The proposed method is validated through two numerical cases and an application example. The results show our method can identify the “bottleneck” stage once the system is reconfigured with saved computational cost.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111093"},"PeriodicalIF":9.4,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824161","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

Costs of a self-sufficient renewable energy community 自给自足的可再生能源社区的成本

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-04 DOI: 10.1016/j.ress.2025.111096

Yanhua Zou , Marko Čepin

引用次数: 0

Digital Twin Model and Platform Based on a Dual System for Control Rod Drive Mechanism Safety 基于双系统的控制杆驱动机构安全数字双模型与平台

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-04 DOI: 10.1016/j.ress.2025.111075

Changfu Wan , Wenqiang Li , Bo Yang , Sitong Ling , Guozhong Fu , Yida Hong

{"title":"Digital Twin Model and Platform Based on a Dual System for Control Rod Drive Mechanism Safety","authors":"Changfu Wan , Wenqiang Li , Bo Yang , Sitong Ling , Guozhong Fu , Yida Hong","doi":"10.1016/j.ress.2025.111075","DOIUrl":"10.1016/j.ress.2025.111075","url":null,"abstract":"<div><div>The digital twin method is a foundational technology for the digitization and intelligence of complex nuclear power equipment, e.g. the control rod drive mechanism. There is an urgent need to develop digital twin modeling methods and application platforms tailored for the safety of complex technical systems. However, current digital twin modeling techniques struggle to meet the requirements for real-time fault monitoring and long-term predictive maintenance simultaneously. Therefore, a five-dimensional digital twin modeling architecture based on a dual system for safety, which combines an offline digital twin system for reliability and fatigue analysis with an online digital twin system for real-time fault monitoring, has been proposed in the current study. The control rod drive mechanism digital twin platform, developed in Isight, is designed to incorporate a dual system for both real-time monitoring and long-term predictive maintenance. A deviation of less than 5% is maintained between operational and experimental data, thus enhancing the reliability and performance of control rod drive mechanism system.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111075"},"PeriodicalIF":9.4,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828797","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

Physics-informed deep learning for virtual rail train trajectory following control 基于物理的深度学习虚拟轨道列车轨迹跟踪控制

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-04 DOI: 10.1016/j.ress.2025.111092

Yuanjin Ji , Youpei Huang , Maozhenning Yang , Han Leng , Lihui Ren , Hongda Liu , Yuejian Chen

{"title":"Physics-informed deep learning for virtual rail train trajectory following control","authors":"Yuanjin Ji , Youpei Huang , Maozhenning Yang , Han Leng , Lihui Ren , Hongda Liu , Yuejian Chen","doi":"10.1016/j.ress.2025.111092","DOIUrl":"10.1016/j.ress.2025.111092","url":null,"abstract":"<div><div>Trajectory-following control is a crucial challenge for virtual rail trains (VRTs), directly impacting tracking accuracy, path width requirements, and operational safety. Traditional model-based control methods, struggle with nonlinear dynamics and require highly accurate system models, while purely data-driven deep learning methods lack physical interpretability and robustness. To address these challenges, this paper proposes a novel Physics-Informed Deep Learning Control Strategy that integrates Lagrangian dynamics equations into a deep neural network, forming a Deep Lagrangian Neural Network (DLNN). This approach ensures that the learned control model retains essential physical properties while capturing complex vehicle dynamics. The DLNN serves as an inverse model within the control framework, mapping desired trajectories to control inputs. Experimental results on circular, lane-change, and obstacle-avoidance maneuvers demonstrate that the DLNN model significantly reduces lateral deviation and yaw rate errors compared to traditional Multi-Layer Perceptron (MLP)-based models. The DLNN exhibits strong generalization capability across different trajectory geometries and benefits from online learning, allowing continuous adaptation to new driving conditions. These findings highlight the potential of physics-informed deep learning in intelligent rail transit systems, providing a more accurate, interpretable, and robust control framework for virtual rail train trajectory following.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111092"},"PeriodicalIF":9.4,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806907","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

Personalized federated learning for remaining useful life prediction under scenarios of fragmented out-of-distribution data 个性化联合学习，在碎片化的非分布数据场景下进行剩余使用寿命预测

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-04 DOI: 10.1016/j.ress.2025.111094

Jiechen Sun, Funa Zhou, Xiong Hu, Chaoge Wang, Tianzhen Wang

{"title":"Personalized federated learning for remaining useful life prediction under scenarios of fragmented out-of-distribution data","authors":"Jiechen Sun, Funa Zhou, Xiong Hu, Chaoge Wang, Tianzhen Wang","doi":"10.1016/j.ress.2025.111094","DOIUrl":"10.1016/j.ress.2025.111094","url":null,"abstract":"<div><div>Accurate Remaining Useful Life (RUL) prediction model relies on full-lifecycle degradation features of the equipment. However, fragmented out-of-distribution (OOD) data due to specific working condition, equipment service time and communication packet loss inevitably affect the prediction accuracy. This study proposes a personalized federated RUL prediction method for fragmented OOD data scenarios, aiming to integrate OOD data fragments provided by different clients. In this means, a federated prediction model can be established to capture the full-lifecycle degradation features by incorporating fragmented OOD data. We focus on establishing a correctable cycle-consistent alignment mechanism driven by health state similarity to solve the challenging problem arisen by inter-client spatiotemporal heterogeneity. A novel health assessment index based on the quantile of hypothesis test is designed to capture the degradation feature required in the cycle-consistent alignment mechanism. Once new fragmented OOD data is available, a personalized federation strategy is developed by designing an adversarial mechanism between degradation features involved in the previous old OOD data and the new OOD data, such that previous degradation features can be further extended to a more full degradation feature. The superiority of the proposed method in RUL prediction was validated on fragmented OOD data collected on benchmark bearing prognostic system (BPS) platform.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111094"},"PeriodicalIF":9.4,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824232","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

Adaptive proposal length scale in Subset Simulation 子集仿真中的自适应提案长度尺度

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-03 DOI: 10.1016/j.ress.2025.111069

Siu-Kui Au, Xin Zhou

{"title":"Adaptive proposal length scale in Subset Simulation","authors":"Siu-Kui Au, Xin Zhou","doi":"10.1016/j.ress.2025.111069","DOIUrl":"10.1016/j.ress.2025.111069","url":null,"abstract":"<div><div>Subset Simulation (SS) is a Monte Carlo method for estimating the failure probability of a system whose response is a ‘black box’, for which little or no prior information is available for variance reduction. Pivotal to SS is an efficient mechanism for generating candidates that are accepted/rejected by Markov Chain Monte Carlo (MCMC) to produce an unbiased estimate. In the standard Normal space, conditional sampling scheme offers an elegant means for generating candidates, reducing the choice of proposal distribution in MCMC to a correlation parameter. Recent developments feature adaptive schemes to achieve some target acceptance rate. For a generic 1-D linear problem, this work obtains analytically the optimal correlation parameter that minimises the lag-1 correlation of samples in a simulation level of SS. Despite the 1-D linear origin, numerical investigations reveal that the resulting adaptive scheme shows promise for effectively suppressing the systematic growth of candidate rejection and correlation along Markov chains for problems of wider context, e.g., with nonlinearity, high dimensions and multiple failure modes. The adaptive scheme exhibits robustness for coping with complex problems where it is difficult to generate failure samples, although efficiency gain in variance reduction may be offset by increased correlation suspectedly between simulation levels. The analytical results derived in this work provide insights on how proposal PDFs should be scaled to cope with rare events.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111069"},"PeriodicalIF":9.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143798594","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 comprehensive belief reliability analysis method for electronic systems considering the effect of failure mechanisms 考虑失效机制影响的电子系统信度综合分析方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-03 DOI: 10.1016/j.ress.2025.111089

Yanfang Wang , Ying Chen , Yingyi Li , Rui Kang

{"title":"A comprehensive belief reliability analysis method for electronic systems considering the effect of failure mechanisms","authors":"Yanfang Wang , Ying Chen , Yingyi Li , Rui Kang","doi":"10.1016/j.ress.2025.111089","DOIUrl":"10.1016/j.ress.2025.111089","url":null,"abstract":"<div><div>Complex electronic systems have been extensively employed in various engineering scenarios, necessitating the use of reasonable and effective methods for analyzing their reliability from the perspective of their performance to ensure smooth and secure operation. While numerous Physics of Failure (PoF)-based methods have identified and modeled failure causes for different system performances, there is still a lack of discussion on the direct correlation between damage caused by failure mechanisms and corresponding performance, as well as consideration of comprehensive performance requirements when analyzing system reliability. To address these issues, this paper proposes a comprehensive belief reliability analysis method for electronic systems to establish a connection between the failure mechanism and performance parameters. An S-O-P (Structure-overload-performance) belief reliability framework has been provided to analyze the reliability of complex electronic systems based on the proposed definitions of structure-related reliability, overload-related reliability and performance reliability. To implement the framework, an Improved Hybrid Bond Graph (IHBG) method is studied, and the interactions and uncertainties of the failure process are quantified. Furthermore, a delay-trigger electronic controller is utilized as an example to demonstrate the effectiveness and rationality of the proposed reliability analysis method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111089"},"PeriodicalIF":9.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806904","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

Surrogate modeling for probability distribution estimation: Uniform or adaptive design? 概率分布估计的代理模型：均匀设计还是自适应设计？

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-03 DOI: 10.1016/j.ress.2025.111059

Maijia Su , Ziqi Wang , Oreste Salvatore Bursi , Marco Broccardo

{"title":"Surrogate modeling for probability distribution estimation: Uniform or adaptive design?","authors":"Maijia Su , Ziqi Wang , Oreste Salvatore Bursi , Marco Broccardo","doi":"10.1016/j.ress.2025.111059","DOIUrl":"10.1016/j.ress.2025.111059","url":null,"abstract":"<div><div>The active learning (AL) technique, one of the state-of-the-art methods for constructing surrogate models, has shown high accuracy and efficiency in forward uncertainty quantification (UQ) analysis. This paper provides a comprehensive study on AL-based global surrogates for computing the full distribution function, i.e., the cumulative distribution function (CDF) and the complementary CDF (CCDF). To this end, we investigate the three essential components for building surrogates, i.e., types of surrogate models, enrichment methods for experimental designs, and stopping criteria. For each component, we choose several representative methods and study their desirable configurations. In addition, we use a uniform design based on maximin-distance criteria as a baseline for measuring the improvement of using AL. Combining all the representative methods, a total of 1920 UQ analyses are carried out to solve 16 benchmark examples. The performance of the selected strategies is evaluated based on accuracy and efficiency. In the context of full distribution estimation, this study concludes that (<em>i</em>) The benefit of using AL is lower than expected and varies across different surrogate models, with three reasons for this performance variability analyzed in detail. (<em>ii</em>) Detailed recommendations are provided for the three surrogate components, depending on the features of the problems (especially the local nonlinearity), target accuracy, and computational budget.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111059"},"PeriodicalIF":9.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777322","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

System reliability evaluation for simultaneous production of orders with varying tolerances 同时生产不同公差订单的系统可靠性评估

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-03 DOI: 10.1016/j.ress.2025.111090

Hsuan-Yu Chen , Yi-Kuei Lin

{"title":"System reliability evaluation for simultaneous production of orders with varying tolerances","authors":"Hsuan-Yu Chen , Yi-Kuei Lin","doi":"10.1016/j.ress.2025.111090","DOIUrl":"10.1016/j.ress.2025.111090","url":null,"abstract":"<div><div>Manufacturers may simultaneously receive orders for a product from different customers, each with varying tolerances for key quality characteristics (KQCs) based on cost and quality considerations. However, previous studies on system reliability evaluation typically classify products as conforming and non-conforming based on a fixed tolerance of each quality characteristic. In order to involve the simultaneous production of a product with multiple tolerances for a specific KQC that significantly impacts its subsequent application, products are classified into conforming grades to determine which specifications meet the KQC tolerances required by the orders. The manufacturing system is first modeled as a multistate flow network (MFN), where nodes represent buffers or quality inspection stations, and arcs represent workstations with identical machines. Each workstation has a multistate capacity depending on the number of malfunctioning or under-maintained machines. Then, a novel algorithm is proposed to calculate the system reliability, the probability that the manufacturing system can fulfill the demands for all grades derived from the orders. Results from numerical experiments provide valuable insights for decision-making in order acceptance and offer a comprehensive assessment of the quality improvements needed to achieve the desired system reliability.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111090"},"PeriodicalIF":9.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824231","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