Reliability Engineering & System Safety最新文献_第6页

Safety assessment of passive safety systems in nuclear reactors using artificial neural networks 核反应堆被动安全系统的人工神经网络安全评价

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-08 DOI: 10.1016/j.ress.2025.111355

Saikat Basak, Lixuan Lu

{"title":"Safety assessment of passive safety systems in nuclear reactors using artificial neural networks","authors":"Saikat Basak, Lixuan Lu","doi":"10.1016/j.ress.2025.111355","DOIUrl":"10.1016/j.ress.2025.111355","url":null,"abstract":"<div><div>This study investigates the application of Artificial Neural Networks (ANNs) for the safety assessment of Passive Safety Systems (PSSs) in nuclear reactors, focusing on mitigating Loss of Coolant Accidents (LOCAs). Using the BWRX-300 Small Modular Reactor (SMR) as an example, the research demonstrates how ANNs can enhance traditional Probabilistic Safety Assessment (PSA) methods. By training ANN models with failure probability data derived from Fault Tree Analysis (FTA), the study predicts failure probabilities of key systems, including the Reactor Isolation (RI) system, Reactor Scram (RS) system, and Isolation Condenser System (ICS). The ANN models successfully captured nonlinear interactions and complex failure scenarios, achieving high prediction accuracy. Additionally, intentional errors introduced into Basic Event (BE) probabilities highlight the ANN's advanced error-handling capabilities, with the models identifying and mitigating discrepancies that FTA failed to address. These findings underscore the potential of ANNs to improve the reliability and safety assessment of nuclear PSSs, offering valuable insights for the development of next-generation reactors.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111355"},"PeriodicalIF":9.4,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144260907","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

Mutual-learning based self-supervised knowledge distillation framework for remaining useful life prediction under variable working condition-induced domain shift scenarios 基于互学习自监督知识精馏框架的变工况域移剩余使用寿命预测

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-08 DOI: 10.1016/j.ress.2025.111359

Zhuohang Chen , Jinglong Chen , Zijun Liu , Yulang Liu

{"title":"Mutual-learning based self-supervised knowledge distillation framework for remaining useful life prediction under variable working condition-induced domain shift scenarios","authors":"Zhuohang Chen , Jinglong Chen , Zijun Liu , Yulang Liu","doi":"10.1016/j.ress.2025.111359","DOIUrl":"10.1016/j.ress.2025.111359","url":null,"abstract":"<div><div>Domain shifts induced by variable working conditions, including both multiple steady and time-varying working conditions, result in inconsistent degradation patterns and pose significant challenges for remaining useful life (RUL) prediction. To address the above issue, we propose a self-supervised knowledge distillation framework based on mutual learning for RUL prediction under variable working conditions. The proposed framework employs a teacher-student architecture, facilitating knowledge transfer through self-supervised pseudo-labels. A mutual learning-based training strategy is developed to prevent over-adaptation to the source domain and promote domain generalization. Additionally, during student model training, a feature-level domain adversarial training strategy is implemented to improve cross-domain feature decoupling and ensure the learning of domain-invariant features. The above two components complement each other: adversarial learning aligns marginal distributions (variable working conditions), while pseudo-label learning refines conditional alignment (normal and fast degradation stages), allowing the model to adapt more effectively to complex degradation scenarios. Furthermore, we incorporate a sparse attention mechanism for efficient feature extraction, significantly reducing computational complexity while maintaining robust performance. The RUL prediction experiments under multi steady conditions and time-varying conditions are carried out on two life-cycle bearing datasets respectively. Comparative results demonstrate the superiority and practicality of our proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111359"},"PeriodicalIF":9.4,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272419","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

Remaining useful life prediction and preventive maintenance approach for multistate manufacturing systems with feedstock heterogeneity 具有原料异质性的多状态制造系统剩余使用寿命预测与预防性维护方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-08 DOI: 10.1016/j.ress.2025.111352

Zhanfei Gao , Jinyong Yao , Yihai He , Hongyan Dui , Zhaolei Liang , Jiao Li

{"title":"Remaining useful life prediction and preventive maintenance approach for multistate manufacturing systems with feedstock heterogeneity","authors":"Zhanfei Gao , Jinyong Yao , Yihai He , Hongyan Dui , Zhaolei Liang , Jiao Li","doi":"10.1016/j.ress.2025.111352","DOIUrl":"10.1016/j.ress.2025.111352","url":null,"abstract":"<div><div>The heterogeneity of feedstock quality can interfere with the performance deterioration of processing machines, which disturbs the reliability degradation of manufacturing systems. Therefore, system reliability modeling considering feedstock heterogeneity is crucial for remaining useful life (RUL) prediction and preventive maintenance of manufacturing systems. According to the RUL prediction model of manufacturing systems considering feedstock heterogeneity, this study proposes a profit-based importance measure evaluation model and utilizes it as a guide for preventive maintenance decision. The proposed method mainly includes the following steps: (1) analyzing the degradation mechanism of manufacturing systems with feedstock heterogeneity and expounding the reliability and RUL connotation of manufacturing systems, (2) proposing a RUL prediction approach for manufacturing systems based on system reliability model considering feedstock heterogeneity, (3) developing a profit-based importance measure model to evaluate the contribution of a machine’s maintenance action to the profit that the system can increase over its RUL, thus determining the maintenance priority of processing machines and optimizing preventive maintenance strategy. The effectiveness and superior performance of proposed approach is verified through an industrial case study of a servo valve spool manufacturing system.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111352"},"PeriodicalIF":9.4,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272418","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 probabilistic framework with recurrent mixture density network for reliability analysis of bridge expansion joint under thermal loading 热荷载作用下桥梁伸缩缝可靠性分析的概率框架-循环混合密度网络

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-07 DOI: 10.1016/j.ress.2025.111341

Yanjia Wang , Dong Yang , Francis T.K. Au

{"title":"A probabilistic framework with recurrent mixture density network for reliability analysis of bridge expansion joint under thermal loading","authors":"Yanjia Wang , Dong Yang , Francis T.K. Au","doi":"10.1016/j.ress.2025.111341","DOIUrl":"10.1016/j.ress.2025.111341","url":null,"abstract":"<div><div>Expansion joints (EJs) are critical components of a bridge to accommodate the temperature-induced movements and prevent structural damage. Predicting the EJ displacements and providing early warnings are crucial to the maintenance and safety of bridges. This paper presents a novel probabilistic framework to predict the EJ displacements, integrating a recurrent mixture density network and Bayesian linear regression. This approach addresses the inherent uncertainties of the measured structural temperatures and linear regression parameters through robust simulations. The Monte Carlo simulation can effectively evaluate the marginal posterior distribution of the EJ displacements. This framework not only derives the critical parameters from the simulations, but also provides the probability distributions associated with the random forecasting errors under significant temperature variations. The recurrent mixture density network, Bayesian linear regression and the combined models, upon examination with different evaluation indicators, prove that the models work well in predicting the probability distributions. The reliability and anomaly indices obtained show that this innovative methodology can provide precise and probabilistic estimation of the factors governing the EJ displacements for steering the early warning systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111341"},"PeriodicalIF":9.4,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298032","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

Intelligent prediction model for pitting corrosion risk in pipelines using developed ResNet and feature reconstruction with interpretability analysis 基于ResNet的管道点蚀风险智能预测模型及特征重构与可解释性分析

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-07 DOI: 10.1016/j.ress.2025.111347

Qiushuang Zheng , Hu Zhang , Hongbing Liu , Hao Xu , Bo Xu , Zhenhao Zhu

{"title":"Intelligent prediction model for pitting corrosion risk in pipelines using developed ResNet and feature reconstruction with interpretability analysis","authors":"Qiushuang Zheng , Hu Zhang , Hongbing Liu , Hao Xu , Bo Xu , Zhenhao Zhu","doi":"10.1016/j.ress.2025.111347","DOIUrl":"10.1016/j.ress.2025.111347","url":null,"abstract":"<div><div>In coastal and offshore environments, oil and gas pipelines are subjected to harsh environmental conditions, including high temperatures, humidity, and salt fog, which accelerate corrosion and deterioration. These factors significantly constrain pipeline lifespan, increase maintenance costs, and pose safety risks. Accurate prediction of corrosion rates is critical for optimizing site selection, construction, and operational strategies—forming a cornerstone of corrosion management in pipeline systems. While existing models predominantly prioritize predictive accuracy, their exploration of the relationships between influencing factors and pipeline pitting depths remains limited. To address this gap, this study introduces an enhanced residual neural network—integrating feature reconstruction—to evaluate pipeline pitting risks. Utilizing Kernel Principal Component Analysis (KPCA) and empirical formulas, the approach identifies key factors most closely correlated with pitting depths. Validation via practical engineering cases demonstrates that the proposed D-ResNet model achieves a MAE of 0.4616, MAPE of 0.3830, and RMSE of 0.5896—reducing errors by 31.6 %, 32.1 %, and 34.9 %, respectively, relative to baseline models. Furthermore, the BowTie framework incorporates SHAP (Shapley Additive exPlanations) analysis to enable interpretable risk characterization, revealing underlying mechanisms and providing a comprehensive methodological basis for lifecycle pipeline integrity management.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111347"},"PeriodicalIF":9.4,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261514","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

Joint optimization of maintenance policy and two-way stock transshipments policy for balanced systems 平衡系统维护策略与双向库存转运策略的联合优化

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-07 DOI: 10.1016/j.ress.2025.111345

Jingjing Wang , Lingyun Luo , Yuxue Jin , Li Yang

{"title":"Joint optimization of maintenance policy and two-way stock transshipments policy for balanced systems","authors":"Jingjing Wang , Lingyun Luo , Yuxue Jin , Li Yang","doi":"10.1016/j.ress.2025.111345","DOIUrl":"10.1016/j.ress.2025.111345","url":null,"abstract":"<div><div>Traditional optimization models of maintenance and inventory policies only focused on a one-way stock transshipment policy (i.e., longitudinal transshipment) of common systems, ignoring the effect of lateral transshipment on maintenance policy. This paper formulated an integrated optimization model of maintenance policy, longitudinal and lateral transshipments for balanced systems. The failure mechanism of balanced systems is different from common systems in the interdependency between the unit and the corresponding unit on the symmetric position. Once a unit fails, the symmetric unit must stop working to keep it balanced. To make full use of the failure characteristics of the balance system, an effective rearrangement policy is proposed to improve the system’s reliability, and replacement actions are conducted to timely correct failed units caused by random environment shocks. To timely replenish the inventory of spare parts, not only a longitudinal order policy (<em>s</em><sub>1</sub>,<em>S</em>) from the depot to any base but also a lateral order policy (<em>Q,s</em><sub>2</sub>) among different bases are simultaneously considered. Since the order times, replacement time and rearrangement time are random variables following general distributions, a semi-Markov decision process framework is utilized to formulate a minimum cost model by selecting appropriate order policies. A modified value-iteration algorithm is proposed to solve the integrated optimization model. Finally, a comparison analysis between with and without the lateral order policy is employed to illustrate the priority of the proposed policy by a simple Unmanned Aerial Vehicle system.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111345"},"PeriodicalIF":9.4,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261517","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 two-layer air traffic dynamic network based en route cascading failure propagation dynamics modeling and multi-dimensional impact analysis 基于航路级联故障传播动力学建模和多维影响分析的两层空中交通动态网络

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-07 DOI: 10.1016/j.ress.2025.111350

Dan Chen , Jianan Yin , Yugang Zhong , Cheng Tang

引用次数: 0

Reliability and safety assessment of distribution networks in mountainous plateau areas subject to low-amplitude lightning 低幅雷击下高原山区配电网可靠性与安全性评价

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-06 DOI: 10.1016/j.ress.2025.111305

Yutao Tang , Kai He , Hongchun Shu , Ke Wang , Weijie Lou , Zhong Qin , Yiming Han , Yue Dai

{"title":"Reliability and safety assessment of distribution networks in mountainous plateau areas subject to low-amplitude lightning","authors":"Yutao Tang , Kai He , Hongchun Shu , Ke Wang , Weijie Lou , Zhong Qin , Yiming Han , Yue Dai","doi":"10.1016/j.ress.2025.111305","DOIUrl":"10.1016/j.ress.2025.111305","url":null,"abstract":"<div><div>Lightning poses a primary threat to the safety and reliability of power systems, especially distribution networks are susceptible to lightning-related accidents due to their weaker protective capabilities. In plateau and mountainous regions, distribution lines are particularly affected by Low-amplitude Lightning (LaL) due to the shielding and attracting effects of mountains and the earth. Analyzing and assessing the overvoltage risks posed by LaL to distribution lines holds significant practical value. This paper develops a risk assessment model for distribution lines in plateau and mountainous regions, firstly, driven by actual lightning data collected by the High Precision Lightning Location System (HPLLS), found that the LaL events exhibited a notably high occurrence frequency of 46.02%. Secondly, a fractal streamlines model (FSM) for simulating the development of lightning leaders is proposed. Finally, based on the FSM, the probability and spatial distribution of LaL direct strikes are calculated, within the lightning strike range, the probability of LaL directly hitting distribution lines was close to 100%, the overvoltage caused by LaL can reach hundreds of kV, and a risk-based LaL damage assessment model is presented. This integrated methodology advances lightning risk assessment practices, providing a physics-informed foundation for enhancing the reliability of mountainous distribution networks.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111305"},"PeriodicalIF":9.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472278","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 predictive maintenance framework based on real-time credibility evaluation of remaining useful life prediction results 基于剩余使用寿命预测结果实时可信度评估的预测性维护框架

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-06 DOI: 10.1016/j.ress.2025.111342

Guannan Shi , Xiaohong Zhang , Jianchao Zeng , Haitao Liao , Jie Gan , Jinhe Wang , Zhijian Wang

{"title":"A predictive maintenance framework based on real-time credibility evaluation of remaining useful life prediction results","authors":"Guannan Shi , Xiaohong Zhang , Jianchao Zeng , Haitao Liao , Jie Gan , Jinhe Wang , Zhijian Wang","doi":"10.1016/j.ress.2025.111342","DOIUrl":"10.1016/j.ress.2025.111342","url":null,"abstract":"<div><div>The increasing availability of remaining useful life (RUL) prediction methods has incentivized the development of predictive maintenance (PdM) for engineering systems. The performance of RUL prediction results is often expected to improve as more condition monitoring data are collected. However, achieving a credible RUL prediction result remains a critical challenge that is often overlooked in current PdM literature. This article proposes a PdM framework to optimize maintenance plans by a PdM utility model correlates the expected maintenance net revenues and losses with the credibility of RUL prediction result to determine the optimal PdM timing. In addition, considering the dynamic characteristics of PdM decision-making driven by condition monitoring data and on the corresponding RUL prediction results, an updating strategy that control the updating frequency is proposed to minimize computational resource waste and avoid decision redundancy. Finally, the proposed PdM framework is validated using the C-MAPSS dataset of turbofan engines.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111342"},"PeriodicalIF":9.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290631","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

Structural reliability analysis based on fractional moments-based iterative maximum entropy method and multiplicative exact dimension reduction integration method 基于分数阶矩迭代最大熵法和乘法精确降维积分法的结构可靠性分析

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-06-06 DOI: 10.1016/j.ress.2025.111344

Lei Wang , Tao Wang , You Dong , Dan M. Frangopol , Zhengliang Li

{"title":"Structural reliability analysis based on fractional moments-based iterative maximum entropy method and multiplicative exact dimension reduction integration method","authors":"Lei Wang , Tao Wang , You Dong , Dan M. Frangopol , Zhengliang Li","doi":"10.1016/j.ress.2025.111344","DOIUrl":"10.1016/j.ress.2025.111344","url":null,"abstract":"<div><div>The fractional moments-based maximum entropy method (FM-MEM) is a powerful method for constructing probability density functions and is widely applied in structural reliability analysis. To ensure the satisfactory performance of this method, it is crucial to determine an appropriate number of constrained fractional moments and estimate them with a balance between accuracy and efficiency. In response to these issues, this paper proposes a fractional moments-based iterative maximum entropy method (FM-IMEM) and a multiplicative exact dimension reduction integration method (M-EDRIM). In the FM-IMEM, a nonlinear transformation is performed to allow negative limit state functions (LSFs), and the optimal number and order of constrained fractional moments and Lagrange multipliers are determined efficiently through an iterative algorithm. Moreover, to estimate fractional moments in the FM-IMEM accurately and efficiently, the M-EDRIM is developed based on a new dimension reduction model, the multiplicative exact dimension reduction model (M-EDRM). This new model can decompose the LSF into a product form of lower-dimensional component functions exactly, thereby alleviating the curse of dimensionality in fractional moments estimation while ensuring accuracy. Five examples with various LSFs are investigated to validate the proposed method. Results show that the proposed method can well balance accuracy and efficiency for structural reliability analysis.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111344"},"PeriodicalIF":9.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272523","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