Reliability Engineering & System Safety最新文献

英文中文

Model-guided system operational reliability assessment based on gradient boosting decision trees and dynamic Bayesian networks

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-20 DOI: 10.1016/j.ress.2025.110949

Yadong Zhang , Shaoping Wang , Enrico Zio , Chao Zhang , Hongyan Dui , Rentong Chen

System reliability assessment is one of the main activities in the operation and maintenance of the industrial sector. If the reliability assessment is inaccurate, it may cause wrong guidance for system maintenance. Although some progress has been made in system reliability assessment, the heavy reliance on data quality and the presence of multiple subsystem state dependencies mean that current purely data-driven methods are unable to fully address these challenges, resulting in limitations in achieving accurate reliability evaluations. In order to improve the accuracy of dynamic system reliability assessment, this paper proposes a hybrid system reliability assessment method that combines gradient boosting decision tree (GBDT) and dynamic Bayesian network (DBN). First, the data generation simulation based on the failure mechanism model is combined with the state diagnosis of the GBDT. Then, monitoring nodes for key components are added to the DBN, and the GBDT is used to establish a mapping relationship between monitoring data and components states. The physical mapping relationships provide objective information, unlike the subjective factors resulting from relying solely on expert experience. The DBN integrates component dependent relationships and monitoring nodes of components to evaluate the system operational reliability. A harmonic gear drive (HGD) system is taken as a case study to verify the proposed method. The results show that the proposed method reduces the relative error percentage in operational reliability assessment by 33 %.

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引用次数: 0

Developing k-out-of-n: G multilevel system with mixed redundancy strategy to protect DSP code using simplified swarm optimization

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-20 DOI: 10.1016/j.ress.2025.110948

Tsung-Jung Hsieh

Mitigating transient faults in aerospace software, particularly radiation-induced Single-Event Upsets (SEUs) affecting Digital Signal Processor (DSP) code, remains a critical challenge. Redundancy strategies are among the most effective approaches to address SEUs. This study introduces a novel program architecture based on a k-out-of-n: G system, incorporating mixed redundancy strategies to enhance the reliability of DSP code. As DSP code operates within a multilevel system, this is the first study to integrate a multilevel system with k-out-of-n: G modules using mixed redundancy strategies. This integration allows for diverse combinations of subsystem reliability calculations, making it strategically advantageous. To evaluate the reliability of redundant program modules in each subsystem, a modular continuous-time Markov chain (CTMC) was applied. To address the extensive combinations of k and n values alongside redundancy strategies, simplified swarm optimization (SSO) was employed for multilevel encoding and near-optimal solution discovery. Experiments on the Fast Fourier Transformation (FFT) program demonstrated the method's effectiveness compared to state-of-the-art approaches, further verifying its scalability and capability to establish a more stable and highly reliable DSP code architecture for large-scale problems.

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引用次数: 0

Cascading failure analysis of interdependent water-power networks based on functional coupling

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-20 DOI: 10.1016/j.ress.2025.110950

Yang Li, Mingyuan Zhang

Due to the increasing interdependence and interconnection, the water supply network (WSN) and electric power network (EPN) face a higher risk of cascading failures. Existing studies mainly focus on the cascading failures of the single network but rarely on the interdependent water-power networks (IWPN) under earthquakes. Therefore, combined with the physical operation characteristics, this paper proposes a cascading failure analysis method for interdependent water-power networks based on functional coupling. First, we define the functional coupling relationships between the IWPN and establish a topology model of the IWPN. Subsequently, the joint probability and functional coupling strength are introduced to determine the failure probability of coupled components in the WSN and EPN. The initial failure components are determined by a random method. Then, the node load function and line capacity function are introduced as the judgment conditions of cascading failure of the WSN and EPN, respectively. The cascading failure transmission process of the WSN and EPN is further conducted based on the dynamical flow model. Further, a calculation method for the functional loss of the WSN and EPN is proposed. Finally, the proposed methodology is applied to the coupling WSN of a certain city and IEEE118 node network. The results show that cascading failures in the IWPN spread wider than a single network and cause more serious functional losses. The findings of this work would have important implications for formulating disaster prevention and mitigation measures and seismic performance improvement strategies for interdependent infrastructure networks.

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引用次数: 0

Efficient seismic reliability and fragility analysis of lifeline networks using subset simulation

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-19 DOI: 10.1016/j.ress.2025.110947

Dongkyu Lee , Ziqi Wang , Junho Song

Various simulation-based and analytical methods have been developed to evaluate the seismic fragilities of individual structures. However, the seismic safety and resilience of a community are substantially affected by network reliability, determined not only by component fragilities but also by network topology and commodity/information flows. However, seismic reliability analyses of networks often encounter significant challenges due to complex network topologies, interdependencies among ground motions, and low failure probabilities. This paper proposes to overcome these challenges by a variance-reduction method for network fragility analysis using subset simulation. The binary network limit-state function in the subset simulation is reformulated into more informative piecewise continuous functions. The proposed limit-state functions quantify the proximity of each sample to a potential network failure domain, thereby enabling the construction of specialized intermediate failure events, which can be utilized in subset simulation and other sequential Monte Carlo approaches. Moreover, by identifying an implicit relationship between intermediate failure events and seismic intensity, we propose a technique to obtain the entire network fragility curve with a single execution of specialized subset simulation. Numerical examples demonstrate that the proposed method can effectively evaluate system-level fragility for large-scale networks.

{"title":"Efficient seismic reliability and fragility analysis of lifeline networks using subset simulation","authors":"Dongkyu Lee , Ziqi Wang , Junho Song","doi":"10.1016/j.ress.2025.110947","DOIUrl":"10.1016/j.ress.2025.110947","url":null,"abstract":"<div><div>Various simulation-based and analytical methods have been developed to evaluate the seismic fragilities of individual structures. However, the seismic safety and resilience of a community are substantially affected by network reliability, determined not only by component fragilities but also by network topology and commodity/information flows. However, seismic reliability analyses of networks often encounter significant challenges due to complex network topologies, interdependencies among ground motions, and low failure probabilities. This paper proposes to overcome these challenges by a variance-reduction method for network fragility analysis using subset simulation. The binary network limit-state function in the subset simulation is reformulated into more informative piecewise continuous functions. The proposed limit-state functions quantify the proximity of each sample to a potential network failure domain, thereby enabling the construction of specialized intermediate failure events, which can be utilized in subset simulation and other sequential Monte Carlo approaches. Moreover, by identifying an implicit relationship between intermediate failure events and seismic intensity, we propose a technique to obtain the entire network fragility curve with a single execution of specialized subset simulation. Numerical examples demonstrate that the proposed method can effectively evaluate system-level fragility for large-scale networks.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110947"},"PeriodicalIF":9.4,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563779","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

Health evaluation techniques towards rotating machinery: A systematic literature review and implementation guideline

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-19 DOI: 10.1016/j.ress.2025.110924

Weixiong Jiang , Jun Wu , Yifan Yang , Xinyu Li , Haiping Zhu

Rotating machinery plays a significant role in fields of manufacturing, energy, aerospace, and so on. Due to harsh environment and heavy load, the rotating machinery are prone to damage during operation process. Thus, health evaluation is critical for the rotating machinery to improve production efficiency, minimize facility downtime, and ensure working safety. At present, the operation and maintenance of the rotating machinery mainly depend on human resources, expert experience, and intelligent algorithm. To our knowledge, few review articles provide a hierarchical guideline about how to select appropriate health evaluation techniques (HETs) for users according to the usage requirement and data availability. To address this issue, this paper systematically reviews the development and application of the HETs adopted in rotating machinery, which are divided into three types: model-based, knowledge-based, and data-driven HETs. Then, the strong and weak points of different HETs are analyzed so as to provide the implementation guideline for selecting the appropriate HETs. Further, current challenges and perspectives are discussed to spark future research of intelligent HETs.

{"title":"Health evaluation techniques towards rotating machinery: A systematic literature review and implementation guideline","authors":"Weixiong Jiang , Jun Wu , Yifan Yang , Xinyu Li , Haiping Zhu","doi":"10.1016/j.ress.2025.110924","DOIUrl":"10.1016/j.ress.2025.110924","url":null,"abstract":"<div><div>Rotating machinery plays a significant role in fields of manufacturing, energy, aerospace, and so on. Due to harsh environment and heavy load, the rotating machinery are prone to damage during operation process. Thus, health evaluation is critical for the rotating machinery to improve production efficiency, minimize facility downtime, and ensure working safety. At present, the operation and maintenance of the rotating machinery mainly depend on human resources, expert experience, and intelligent algorithm. To our knowledge, few review articles provide a hierarchical guideline about how to select appropriate health evaluation techniques (HETs) for users according to the usage requirement and data availability. To address this issue, this paper systematically reviews the development and application of the HETs adopted in rotating machinery, which are divided into three types: model-based, knowledge-based, and data-driven HETs. Then, the strong and weak points of different HETs are analyzed so as to provide the implementation guideline for selecting the appropriate HETs. Further, current challenges and perspectives are discussed to spark future research of intelligent HETs.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110924"},"PeriodicalIF":9.4,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547943","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

State space neural network with nonlinear physics for mechanical system modeling 用于机械系统建模的非线性物理状态空间神经网络

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-19 DOI: 10.1016/j.ress.2025.110946

Reese Eischens , Tao Li , Gregory W. Vogl , Yi Cai , Yongzhi Qu

Dynamic modeling of mechanical systems is important for the monitoring, diagnostics, control, and prediction of system behaviors. Modeling dynamic systems is one of the emerging tasks in scientific machine learning. Neural networks have been used to learn surrogate models for the underlying dynamics in the form of data-driven neural ordinary differential equations (NODEs). While most dynamical mechanical systems have some degree of nonlinearity within their dynamics, neural networks have shown potential in approximating dynamic systems with nonlinearities. However, despite the universal approximation capability of neural networks, this paper argues that by adding physics-aware nonlinear functions to the neural network model, the modeling accuracy of the neural network can be increased. In this paper, the construction of the nonlinear continuous-time state-space neural network (NLCSNN) is presented. The proposed approach can be used as a data-driven method for digital twin construction for monitoring, prediction, and reliability assessment. The NLCSNN improves upon the previously established continuous-time state-space neural network by increasing sensitivity to nonlinearity. The proposed NLCSNN is trained and validated using numerical and experimental examples, with results compared against those from several existing methodologies. Validation results show that the NLCSNN model can learn complex engineering dynamics without explicit knowledge of the underlying system. The modeling performance of the proposed data-driven approach outperforms a purely physics-based model, with results comparable to hybrid models. Additionally, the NLCSNN model achieved higher accuracy than the continuous-time state-space neural network (CSNN) model.

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引用次数: 0

Lost capacity of the weighted k-out-of-n system with discrete component lifetimes

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-18 DOI: 10.1016/j.ress.2025.110899

Agnieszka Goroncy , Krzysztof Jasiński , Faustyna Korejwo , Marta Rudzate

In this paper we consider a weighted

k

-out-of-

n

system. Each component has a positive integer-valued weight assigned interpreted as its total capacity. The system is in a working state if the accumulated weights of all working components are at least

k

. The component lifetimes may be dependent and non-identically discretely distributed random variables. The primary focus is the capacity lost by the system upon its failure, for which we derive the probability mass function. This quantity has a potential that enables optimal system design. We also provide two numerical examples which give a demonstration of the theoretical results.

引用次数: 0

Resilience assessment of High-speed railway networks from the spatio-temporal perspective: A case study in Jiangsu Province, China

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-18 DOI: 10.1016/j.ress.2025.110900

Yunjiang Xiao , Yang Li , Weidong Liu , Zhiyuan Wang , Jun Chen , Wei Wang

High-speed railways (HSR) are susceptible to disruptions due to a variety of factors such as extreme weather. Improving the resilience of HSR is crucial for minimizing losses and improving operation efficiency. This paper aims to strengthen the resilience of HSR by reducing network vulnerability and enhancing network reliability. An HSR spatio-temporal network (HSRSN) model is constructed to simulate trains’ operation on railways. The model is grounded in the train timetable, combining infrastructure networks and train operations. Critical trains and critical nodes are components that exhibit reduced resilience when the network is subjected to disruptions. Percolation theory is used to identify the critical trains and the information entropy algorithm is introduced for identifying critical nodes. Additionally, a typhoon occurrence is chosen as the disruption for analyzing network vulnerability and connectivity. As for recovery post-disruptions, a strategy is proposed that utilizes timetable adjustments to mitigate the delays caused by disturbances. The performance of the proposed methods has been demonstrated in the case of the HSR network in Jiangsu Province, China. Results show that suspending critical trains during 13:00–15:00 and 17:00–19:00 would significantly reduce the network’s connectivity. Network vulnerability is correlated with both the information entropy of nodes and the timing of link occurrences.

{"title":"Resilience assessment of High-speed railway networks from the spatio-temporal perspective: A case study in Jiangsu Province, China","authors":"Yunjiang Xiao , Yang Li , Weidong Liu , Zhiyuan Wang , Jun Chen , Wei Wang","doi":"10.1016/j.ress.2025.110900","DOIUrl":"10.1016/j.ress.2025.110900","url":null,"abstract":"<div><div>High-speed railways (HSR) are susceptible to disruptions due to a variety of factors such as extreme weather. Improving the resilience of HSR is crucial for minimizing losses and improving operation efficiency. This paper aims to strengthen the resilience of HSR by reducing network vulnerability and enhancing network reliability. An HSR spatio-temporal network (HSRSN) model is constructed to simulate trains’ operation on railways. The model is grounded in the train timetable, combining infrastructure networks and train operations. Critical trains and critical nodes are components that exhibit reduced resilience when the network is subjected to disruptions. Percolation theory is used to identify the critical trains and the information entropy algorithm is introduced for identifying critical nodes. Additionally, a typhoon occurrence is chosen as the disruption for analyzing network vulnerability and connectivity. As for recovery post-disruptions, a strategy is proposed that utilizes timetable adjustments to mitigate the delays caused by disturbances. The performance of the proposed methods has been demonstrated in the case of the HSR network in Jiangsu Province, China. Results show that suspending critical trains during 13:00–15:00 and 17:00–19:00 would significantly reduce the network’s connectivity. Network vulnerability is correlated with both the information entropy of nodes and the timing of link occurrences.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110900"},"PeriodicalIF":9.4,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471223","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

Failure prediction of overhead transmission lines incorporating time series prediction model for wind-ice loads

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-18 DOI: 10.1016/j.ress.2025.110927

Xiangrui Meng, Li Tian, Juncai Liu, Qingtong Jin

The existing research on transmission line icing usually considers the climatic conditions of the past period in the form of return period. However, in the era of climate change, it is impossible to fully predict the disaster intensity of future structures with historical data. Therefore, this paper proposes an framework for regional overhead transmission lines (OTLs) under wind-ice. Firstly, the meteorological station data of the OTL area are collected, and the key meteorological parameters of the current and future periods are calculated based on the statistical data and time series prediction model. Subsequently, based on the proposed calculation method of wind-ice combined action, the disaster intensity and OTL response in each historical period are studied. Finally, the fragility of OTL under the combined action of wind and ice is calculated, and the failure probability of OTL under different historical periods is calculated by combining the distribution of wind and ice disasters with the fragility of OTL. The results show that climate change seriously affects the failure probability of the structure, resulting in greater uncertainty in the OTL life cycle, and a supplementary design strategy for coping with climate change is recommended.

{"title":"Failure prediction of overhead transmission lines incorporating time series prediction model for wind-ice loads","authors":"Xiangrui Meng, Li Tian, Juncai Liu, Qingtong Jin","doi":"10.1016/j.ress.2025.110927","DOIUrl":"10.1016/j.ress.2025.110927","url":null,"abstract":"<div><div>The existing research on transmission line icing usually considers the climatic conditions of the past period in the form of return period. However, in the era of climate change, it is impossible to fully predict the disaster intensity of future structures with historical data. Therefore, this paper proposes an framework for regional overhead transmission lines (OTLs) under wind-ice. Firstly, the meteorological station data of the OTL area are collected, and the key meteorological parameters of the current and future periods are calculated based on the statistical data and time series prediction model. Subsequently, based on the proposed calculation method of wind-ice combined action, the disaster intensity and OTL response in each historical period are studied. Finally, the fragility of OTL under the combined action of wind and ice is calculated, and the failure probability of OTL under different historical periods is calculated by combining the distribution of wind and ice disasters with the fragility of OTL. The results show that climate change seriously affects the failure probability of the structure, resulting in greater uncertainty in the OTL life cycle, and a supplementary design strategy for coping with climate change is recommended.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110927"},"PeriodicalIF":9.4,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454665","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

Reliability analysis for multi-component system considering failure propagation and dependent competing failure process 考虑故障传播和依赖性竞争故障过程的多组件系统可靠性分析

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-02-18 DOI: 10.1016/j.ress.2025.110930

Hao Lyu , Zhihang Li , Xuehang Qiao , Bing Lu , Hualong Xie , Michael Pecht

Failure propagation exists widely in complex systems and has great influence on reliability evaluation of systems. A system reliability model considering multiple failure propagation based on the dependent competing failure process is developed in this paper. A multiple failure propagation is explored, namely, acceleration of degradation and intensification of shock. General linear paths with different degradation rates and shock damage are used to describe the soft failure process of components. The mixed shock model and the Markov chain method are used to establish the reliability model of the hard failure process. The discrete probability density function method is used to calculate component failure time. The reliability model of the whole system is derived by utilizing the total probability method. The model is verified by using a Li-ion battery pack as a numerical example, and the corresponding analysis is conducted.

引用次数: 0

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Reliability Engineering & System Safety

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