Reliability Engineering & System Safety最新文献

{"title":"Two-stage failure probability function estimation method based on improved cross-entropy importance sampling and adaptive Kriging","authors":"Xin Fan ,&nbsp;Xufeng Yang ,&nbsp;Yongshou Liu","doi":"10.1016/j.ress.2025.111272","DOIUrl":"10.1016/j.ress.2025.111272","url":null,"abstract":"<div><div>In structural reliability design, determining distribution parameters of uncertainty variables is essential for minimizing failure probability, expressed as the failure probability function (FPF). Existing FPF estimation methods face challenges in computational accuracy and efficiency. This paper enhances the improved cross-entropy importance sampling (ICE-IS) method and proposes AICE-IS for FPF estimation in the augmented space and OICE-IS for FPF estimation in the original space. To enhance the efficiency of active learning, this paper proposes the global entropy reduction (GER) learning function. Subsequently, the GER learning function and Kriging were integrated with AICE-IS and OICE-IS, respectively, leading to the development of the two-stage FPF estimation methods ALK-AICE and ALK-OICE, which are suitable for expensive finite element problems. The performance of the GER learning function was validated across three benchmark examples, while ALK-AICE and ALK-OICE demonstrated efficiency and accuracy in four numerical examples. These methods were further applied to resonance reliability design of axially functionally graded material (FGM) pipes and aircraft landing gear impact reliability analysis.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111272"},"PeriodicalIF":9.4,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144177699","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}

{"title":"Dynamic modeling-assisted tensor regression transfer learning for online remaining useful life prediction under open environment","authors":"Wentao Mao ,&nbsp;Jiayi Wang ,&nbsp;Ke Feng ,&nbsp;Zhidan Zhong ,&nbsp;Mingjian Zuo","doi":"10.1016/j.ress.2025.111210","DOIUrl":"10.1016/j.ress.2025.111210","url":null,"abstract":"<div><div>Dynamic online fault prognosis or prediction of the remaining useful life (RUL) of machinery with sequentially-collected monitoring data is of great significance for assurance of safety, reliability, and economic operation of engineering systems. Under open environment, however, online fault prognosis faces two challenges: (1) Distribution of degradation data tends to be inconsistent across different machines, and (2) Data distribution of the target machine may drift due to change of its operating condition. To address these two concerns, this paper takes rolling bearing as the study object and proposes a new dynamic model-assisted tensor regression transfer learning method for online RUL prediction. The key idea is to integrate the mechanism information of the physics-based simulation model and the self-supervised information of online data in the prognosis process. This proposed method includes two stages: pre-training and online prediction. In the pre-training stage, a deep tensor domain-adversarial model is constructed using offline degradation data and available online data. Meanwhile, a simulation library with different damage scales and degradation rates is established based on a five degree-of-freedom dynamic model. In the second online prediction stage, the prediction model is initialized by the pre-trained network obtained from the first stage. For each online data block collected from the target bearing, self-supervised information in terms of monotonicity is extracted through core tensor, while the data with the highest similarity is selected from the simulation library to extract mechanism information. An alternating optimization algorithm is then constructed to dynamically update the online prediction model through integrating these two kinds of information. Moreover, the paper provides a theoretical upper bound of the generalization error for model-data-fusion RUL prediction, proving that the transfer strategy utilizing mechanism information can definitely reduce the prognosis error. Experimental results on three bearing run-to-failure datasets demonstrate the effectiveness of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111210"},"PeriodicalIF":9.4,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135160","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}

{"title":"A new weighted rough set and improved BP neural network method for predicting forest fires","authors":"Enhui Zhao ,&nbsp;Ning Wang ,&nbsp;Shibo Cui ,&nbsp;Rui Zhao ,&nbsp;Yongping Yu","doi":"10.1016/j.ress.2025.111206","DOIUrl":"10.1016/j.ress.2025.111206","url":null,"abstract":"<div><div>To solve the quality problems of redundant risk elements, data imbalance, and noisy samples, which are commonly found in forest fire datasets, and to further improve the accuracy of forest fire risk prediction. In this paper, a forest fire prediction method is proposed, which combines a probability-weighted rough set attribute reduction (PWRS-AR) strategy with a particle swarm optimization improved BP neural network (PSO-I-BPNN) for forest fire prediction. Firstly, a probabilistic weighted rough set attribute reduction method is designed to effectively eliminate non-critical and redundant features in the dataset and simplify the input space of the neural network. Subsequently, a particle swarm optimization (PSO) algorithm is employed to refine the BP neural network (BPNN), aiming to elevate both the precision and efficiency of forest fire prediction. To validate the method’s effectiveness, experiments are conducted on three representative forest fire datasets. The results show that compared with the traditional machine learning prediction methods, the proposed forest fire prediction model achieves a significant improvement in prediction accuracy and is more suitable for early warning and disaster prevention and mitigation strategies in forest fire-prone areas.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111206"},"PeriodicalIF":9.4,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135163","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}

{"title":"Network analysis-enhanced project risk management for nuclear power plant construction","authors":"André L.N. Casotti ,&nbsp;Enrico Zio","doi":"10.1016/j.ress.2025.111269","DOIUrl":"10.1016/j.ress.2025.111269","url":null,"abstract":"<div><div>This paper introduces a comprehensive framework for managing interdependent delay risks in nuclear power plant (NPP) construction by integrating network theory and topological analysis. Spent fuel disposal, nuclear plant safety and nuclear weapons proliferation are known important concerns for nuclear power development, but costs remain the fundamental problem, as NPP projects are plagued by schedule delays that substantially increase total costs. Such complex megaprojects are exposed to numerous risks of different sources that behave interdependently. Most of the studies understand the risks of delay in NPP construction projects in isolation without taking interdependencies into account. The proposed methodology employs a Design Structure Matrix (DSM) to construct a Risk Interaction Network (RIN), enabling a topological assessment to identify critical risks that may cause cascading delays in project tasks. An algorithmic search for these critical risks is conducted, considering the impact of their removal on the RIN's characteristics. We define a bi-objective optimization problem aimed at generating a project schedule that minimizes both the project's makespan and the reachability density of the RIN. The solution is obtained using an evolutionary algorithm. Applied to a Double-Containment Pressurized Water Reactor (DC-PWR) project, this approach effectively uncovers risks neglected by classical analysis and offers scheduling options for different risk attitudes, enhancing decision-making capabilities.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111269"},"PeriodicalIF":9.4,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139429","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}

{"title":"A reliable bearing remaining useful life prediction method based on multi-hierarchy dynamic evaluation and uncertainty amelioration","authors":"Wenjie Li ,&nbsp;Dongdong Liu ,&nbsp;Xin Wang ,&nbsp;Lingli Cui","doi":"10.1016/j.ress.2025.111270","DOIUrl":"10.1016/j.ress.2025.111270","url":null,"abstract":"<div><div>Due to the synergistic effect of internal and external factors, the degradation process of bearings exhibits strong nonlinearity and high uncertainty, which poses significant challenges for condition monitoring and remaining useful life (RUL) prediction of bearings. Therefore, a reliable RUL prediction method based on multi-hierarchy dynamic evaluation and uncertainty amelioration is proposed in this paper. First, the degradation pattern of the bearing is adaptively determined according to the real-time monitoring data, thereby reducing the reliance on domain-specific prior knowledge of bearing degradation. Subsequently, the health status is iteratively updated with a multi-hierarchy dynamic evaluation mechanism, while a dual-source feedback fine-tuning strategy is designed to collaboratively enhance the model’s predictive performance in real time. Finally, a lifetime uncertainty amelioration technique is developed to integrate lifetime information encoded in uncertainty distributions across multiple hierarchical levels, thereby enhancing the reliability of prediction results. To validate the performance of the proposed method, a comparison with several peer methods is conducted in two experimental bearing datasets, and the outcomes indicate that the proposed method exhibits high accuracy and great reliability.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111270"},"PeriodicalIF":9.4,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144177698","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}

{"title":"The impact of protection devices on the availability of low-voltage direct current microgrids","authors":"Glenn Emmers ,&nbsp;Tom Van Acker ,&nbsp;Simon Ravyts ,&nbsp;Johan Driesen","doi":"10.1016/j.ress.2025.111190","DOIUrl":"10.1016/j.ress.2025.111190","url":null,"abstract":"<div><div>This paper presents a method to determine the impact of low-voltage direct current circuit breakers on the availability of the dc bus. Low-voltage direct current is gaining traction in industry, and protection is an important aspect. New circuit breaker technologies are extensively researched, but the impact of the speed of interruption on direct current bus availability has not yet been investigated. This paper demonstrates how to determine the fault clearance probability, which is used to set up state transition diagrams for the feeder states. These state transition diagrams are solved using the semi-Markov process. The solutions of the semi-Markov process are used in the UGO method to determine the availability of the direct current bus. Furthermore, the results depend on the length of the feeder, the capacitance of the direct current bus and the minimum allowed voltage. For feeders with a fault clearance probability lower than 100%, the number of feeders also influences the availability of the direct current bus.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"262 ","pages":"Article 111190"},"PeriodicalIF":9.4,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098964","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}

{"title":"Evidential reasoning rule with dynamic correlation for system reliability prediction","authors":"Jie Wang ,&nbsp;Zhijie Zhou ,&nbsp;Zheng Lian ,&nbsp;Yue Han","doi":"10.1016/j.ress.2025.111267","DOIUrl":"10.1016/j.ress.2025.111267","url":null,"abstract":"<div><div>In engineering, reliability prediction holds crucial significance for ensuring the normal operation of complex systems. Since the reliability prediction involves both quantitative data and qualitative knowledge, the evidential reasoning (ER) rule emerges as a promising prediction approach. However, the ER rule-based prediction model assumes that there is independence or static correlation between different past instants, which is inconsistent with engineering practice. In light of this, a new prediction model based on the ER rule with dynamic correlation is proposed in this paper. In this model, the exponential distributions with temporal information are employed to describe the dynamic correlations between different time instants. Subsequently, the dynamic correlations are utilized to discount the initial evidence and the prediction results are obtained through the nonlinear fusion of multiple pieces of evidence. Besides, several interpretability criteria are set after analyzing the physical meanings of model parameters. Moreover, these criteria are transformed into corresponding parameter constraints, contributing to establishing an optimization objective with interpretability. This can ensure the prediction accuracy while preserving the model interpretability as much as possible. Two engineering examples are carried out to verify the validity of the proposed model.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"262 ","pages":"Article 111267"},"PeriodicalIF":9.4,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116983","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}

{"title":"GPU-accelerated approaches for multi-unit level 3 PSA: Focusing on dispersion, evacuation, and dose assessment","authors":"Juryong Park ,&nbsp;Siho Jang ,&nbsp;Sung-yeop Kim ,&nbsp;Eung Soo Kim","doi":"10.1016/j.ress.2025.111268","DOIUrl":"10.1016/j.ress.2025.111268","url":null,"abstract":"<div><div>This study proposes a Graphics Processing Unit (GPU)-accelerated computational framework aimed at addressing the significant computational challenges inherent in key modules of Level 3 Probabilistic Safety Assessment (Level 3 PSA) for multi-unit nuclear power plant accidents. Specifically, it focuses on three of the most computationally intensive processes—atmospheric dispersion modeling, agent-based evacuation dynamics, and radiological dose assessment. By integrating these modules into a unified, scalable simulation environment, the proposed GPU-based framework can simultaneously and efficiently handle numerous meteorological datasets, evacuee groups, nuclear power plant units, and Gaussian puffs, significantly enhancing computational performance compared to existing methodologies. Comparative performance evaluations demonstrate that this approach achieves speedups of over 1400 times relative to traditional Central Processing Unit (CPU)-based methods, effectively supporting near-real-time emergency response and precise risk assessments. Although additional aspects of Level 3 PSA—such as ingestion exposure pathways and economic impact assessments—are important, they are not within the scope of this study and can be addressed separately as post-processing or through dedicated modules. Instead, this study focuses on demonstrating the necessity and effectiveness of GPU acceleration in accurately capturing the spatiotemporal complexity and radiological release characteristics of multi-unit nuclear accidents.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111268"},"PeriodicalIF":9.4,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139428","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}

{"title":"Mitigating domain shift problems in data-driven risk assessment models","authors":"Yining Wang ,&nbsp;Zhenji Zhang ,&nbsp;Daqing Gong ,&nbsp;Gang Xue","doi":"10.1016/j.ress.2025.111263","DOIUrl":"10.1016/j.ress.2025.111263","url":null,"abstract":"<div><div>This paper presents a domain adaptation algorithm that combines adversarial feature alignment and cycle-consistency restoration to address the domain shift problem in disaster risk assessment. By using adversarial networks, the model adapts features at the feature level, effectively leveraging unlabelled data, reducing the cost of data labelling, and minimizing the feature distribution differences between the source and target domains. Additionally, the introduction of cycle-consistency verification ensures the accuracy and consistency of feature transformation. The experimental results demonstrate that this algorithm performs exceptionally well in multiple real-world disaster risk assessment scenarios, significantly improving the accuracy and reliability of risk assessments compared with existing domain adaptation techniques. The key contributions of this research are as follows: (1) Utilizing adversarial learning to enable unsupervised domain adaptation, significantly reducing the need for labelled data and improving model adaptability in new environments; (2) introducing a training consistency-based adversarial learning method to preserve key information during domain adaptation, improving generalization in new domains; and (3) effectively addressing domain shift, enhancing model adaptability, and providing data-driven support for downstream decision-making, reducing disaster risk and resource waste. This approach not only advances disaster risk assessment but also promotes the broader application of unsupervised domain adaptation in various fields requiring fast and effective adaptation.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111263"},"PeriodicalIF":9.4,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135155","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}

{"title":"A Bayesian piecewise fitting method for estimating probability distributions of performance functions","authors":"Yan-Gang Zhao ,&nbsp;Ya-Ting Liu ,&nbsp;Pei-Pei Li ,&nbsp;Ye-Yao Weng ,&nbsp;Marcos A. Valdebenito ,&nbsp;Matthias G.R. Faes","doi":"10.1016/j.ress.2025.111266","DOIUrl":"10.1016/j.ress.2025.111266","url":null,"abstract":"<div><div>The probability distribution of the performance function plays an important role in many fields. However, it is challenging to obtain this distribution because of the difficulty in capturing the tails on both sides, particularly for high-dimensional problems. To estimate the probability distribution of the performance function efficiently and accurately, this study proposes a piecewise fitting method based on the simulation-based Bayesian post-processing method. The method first divides the whole distribution into the main body and the left and right tail distributions. Subsequently, the samples for the main body are generated by a randomized Sobol sequence, while the samples for the left and right tails are produced through Markov chain Monte Carlo sampling. Thereafter, the shifted generalized lognormal distribution model is applied to reconstruct the main body distribution, and the truncated shifted generalized lognormal distribution is used to fit the tail distributions. Finally, the overall distribution is obtained, and the shape parameters of the distribution model are determined using Bayesian estimation methods. The efficiency and accuracy of the proposed method are demonstrated through four numerical examples, including a simple toy example and cases involving strongly nonlinear, implicit, high-dimensional performance functions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111266"},"PeriodicalIF":9.4,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147877","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}