Reliability Engineering & System Safety最新文献_第9页

Uncertainty aware federated averaging approach for privacy secured collaborative remaining useful life prediction of rolling element bearing 基于不确定性的滚动轴承隐私安全协同剩余使用寿命预测联合平均方法

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-07-01 Epub Date: 2026-01-17 DOI: 10.1016/j.ress.2026.112221

Wasib Ul Navid , Khandaker Noman , Khandaker Ashfak , Yongbo Li , Zhe Su , Anayet Ullah Patwari

{"title":"Uncertainty aware federated averaging approach for privacy secured collaborative remaining useful life prediction of rolling element bearing","authors":"Wasib Ul Navid , Khandaker Noman , Khandaker Ashfak , Yongbo Li , Zhe Su , Anayet Ullah Patwari","doi":"10.1016/j.ress.2026.112221","DOIUrl":"10.1016/j.ress.2026.112221","url":null,"abstract":"<div><div>Centralized prediction of remaining useful life (RUL) has demonstrated promising result during predictive maintenance of rolling element bearing. However, centralized learning paradigms for RUL prediction of bearings face significant challenges in industrial scenarios. Firstly, sufficient life-cycle degradation data is difficult to obtain from a single-edge client. Secondly, concerns related to copyright issue contribute to the continued isolation of user data. Thirdly, state-of-the-art methods often overlook integrating uncertainty as feedback to enhance predictive learning for reliable RUL estimation. To address these challenges, this article proposes an uncertainty-aware federated averaging (UAFA) approach within a federated learning framework. Firstly, in the framework, stochastic gradient descent is performed with each local bearing client by monte-carlo dropout (MCD) based long short-term memory network. During local training, a dynamic modulation factor is used to adapt the uncertainty-aware learning rate and the uncertainty components are sent to the central server upon training completion. Finally, client models are aggregated using UAFA and evaluated on multiple bearing datasets. Experiments on several run-to-failure tests show that the UAFA-based framework achieves higher accuracy and lower uncertainty than state-of-the-art (SOTA) aggregation methods. Moreover, UAFA consistently outperforms existing approaches across diverse feature types and client counts, demonstrating strong robustness and generalizability.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112221"},"PeriodicalIF":11.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079546","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

Knowledge-data fusion for water supply pipe failure prediction: A hybrid physics-informed and data-driven method 供水管道故障预测的知识数据融合：一种物理信息和数据驱动的混合方法

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-07-01 Epub Date: 2026-01-23 DOI: 10.1016/j.ress.2026.112263

Qunfang Hu , Qiang Zhang , Delu Che , Fei Wang , Zongyuan Zhang , Jiahua Zhou

{"title":"Knowledge-data fusion for water supply pipe failure prediction: A hybrid physics-informed and data-driven method","authors":"Qunfang Hu , Qiang Zhang , Delu Che , Fei Wang , Zongyuan Zhang , Jiahua Zhou","doi":"10.1016/j.ress.2026.112263","DOIUrl":"10.1016/j.ress.2026.112263","url":null,"abstract":"<div><div>Pipe failure prediction is critical for daily maintenance and asset management of water distribution networks (WDNs). As the mainstream paradigm for pipe failure modeling, data-driven machine learning (ML) methods are limited by the sparsity of operational data in WDNs and lack physical interpretability. This study proposes a hybrid physics-informed and data-driven method integrating mechanical knowledge with operational data to improve pipe failure prediction. The hybrid method adopts the ML model as its primary architecture, under which a mechanical approach is incorporated to embed the structural safety factor of pipes as an extended physical feature into the feature space of the ML model. The proposed hybrid method is applied to predict pipe failures of a large WDN in China. The results demonstrate that the hybrid models deliver superior predictive capacity and cost-effectiveness compared to pure ML models, achieving significant improvements across various evaluation metrics. The extended physical feature plays a crucial role in pipe failure prediction, with its contribution to the model's predictions aligning with established mechanical principles. Additionally, pipes crossing roads at oblique angles and those located at road intersections are more prone to failure. This research provides insights for improving management strategies and resilience in WDNs.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112263"},"PeriodicalIF":11.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079479","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

Future directions for data-driven approaches in pipeline integrity management: Risk assessment, in-line inspection, and machine learning 管道完整性管理中数据驱动方法的未来发展方向：风险评估、在线检查和机器学习

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-07-01 Epub Date: 2026-01-27 DOI: 10.1016/j.ress.2026.112300

Tim Bastek , Jens Denecke , Jürgen Schmidt

{"title":"Future directions for data-driven approaches in pipeline integrity management: Risk assessment, in-line inspection, and machine learning","authors":"Tim Bastek , Jens Denecke , Jürgen Schmidt","doi":"10.1016/j.ress.2026.112300","DOIUrl":"10.1016/j.ress.2026.112300","url":null,"abstract":"<div><div>Gas pipeline failure continues to be a serious hazard for people in the vicinity of gas pipelines, particularly given the increase in urban development and aging infrastructure. This study critically reviews the current state and potential of data-driven approaches in pipeline integrity management systems (PIMS) for most critical threats. In addition to a purely theoretical discussion, three illustrative case studies are used to highlight the main limitations in the following areas: a) third-party damage assessment, b) the quality of in-line-Inspection (ILI) data and c) machine learning-based external corrosion evaluation. A quantitative risk analysis was performed to analyze shortcomings in context of current prevention practices. Research gaps lie in the evaluation of probability of failure insufficiently dependent on the gas pipeline location but in practice on pipeline design. A new GIS-based, probabilistic approach was proposed to assess TPD using available environmental data. Secondly, published ILI data was analyzed, which reveals a large amount of corrosion detected over pipeline route, but low replicability from one ILI run to another - limiting usage in PIMS and data driven modelling. Thirdly, a hybrid support vector regression model was trained to predict external corrosion, but its performance proved unstable: prediction accuracy dropped by 27% during cross-validation, highlighting the practical risks of model overfitting. This study highlights the need for more robust, context-sensitive models and outlines potential advancements to improve pipeline safety and system reliability using data-driven strategies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112300"},"PeriodicalIF":11.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079474","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

Transformer-augmented deep Q-network-based risk-informed maintenance policy for partially observable systems under combined degradation and random shock effects 退化和随机冲击联合作用下部分可观测系统的基于变压器增强深度q网络的风险知情维护策略

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-07-01 Epub Date: 2026-01-28 DOI: 10.1016/j.ress.2026.112301

Chunhui Guo, Zhenglin Liang

{"title":"Transformer-augmented deep Q-network-based risk-informed maintenance policy for partially observable systems under combined degradation and random shock effects","authors":"Chunhui Guo, Zhenglin Liang","doi":"10.1016/j.ress.2026.112301","DOIUrl":"10.1016/j.ress.2026.112301","url":null,"abstract":"<div><div>Many real-world systems experience both natural degradation and random shocks, with degradation often assessed using only partial information. When both factors are considered, the underlying degradation process may carry a high risk of transitioning rapidly to a more severe state, making the interpretation of partial observations particularly challenging. To address this challenge, we formulate partially observable systems under combined natural degradation and random shock effects as a partially observable continuous-time Markov model. Based on this model, we introduce a risk-informed inspection and maintenance policy that schedules inspections according to a predefined risk threshold, aiming to reduce costs. We demonstrate that the optimal maintenance approach follows a control-limit policy, applied at decision epochs determined by the evolving risk profile. Leveraging this structural insight, we design a tailored Transformer-augmented Deep Q-Network algorithm to effectively optimize the inspection and maintenance policy under partial observation, which is regarded as a novel and online algorithm for the Partially Observable Markov Decision Process with a multi-dimensional continuous state space. The proposed approach is validated through a case study involving lithium-ion battery maintenance. The results reveal that our approach achieves an average reduction of 57.4% in inspection costs compared to traditional periodic inspection schemes.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112301"},"PeriodicalIF":11.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079556","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

Dynamic SCGE–NEG for construction reliability: Probabilistic decision support for transport upgrades, work-zone operations, and regional labor mobility 建筑可靠性的动态SCGE-NEG：运输升级、工作区操作和区域劳动力流动的概率决策支持

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-07-01 Epub Date: 2026-01-22 DOI: 10.1016/j.ress.2026.112268

Ali Shehadeh, Musab Abuaddous, Hamsa F. Nimer

{"title":"Dynamic SCGE–NEG for construction reliability: Probabilistic decision support for transport upgrades, work-zone operations, and regional labor mobility","authors":"Ali Shehadeh, Musab Abuaddous, Hamsa F. Nimer","doi":"10.1016/j.ress.2026.112268","DOIUrl":"10.1016/j.ress.2026.112268","url":null,"abstract":"<div><div>We develop a multi-region SCGE–NEG model that embeds a capacity-constrained construction sector and time-critical logistics into a transport economy with endogenous migration and agglomeration externalities. Transport performance enters delivery prices via iceberg costs derived from network speeds and stochastic travel-time reliability. Construction production uses labor, equipment, and intermediate inputs (cement, aggregates, steel, bitumen) with queue-based delivery windows; late deliveries incur reliability penalties. We fuse probe-based travel-time distributions with multi-year e-ticketing records for hot-mix asphalt and aggregates, applying robust preprocessing to handle outliers and sensor noise (spec-based filters, trimming and winsorization of abnormal temperatures and weights, and cross-checks against project logs). A short–medium–long run solution cycle (goods, equilibrium, migration, and capital/entry) evaluates two policy families: (1) corridor upgrades & phasing, and (2) work-zone traffic management during project execution. Using a prefecture-scale testbed (47 regions; multi-sector IO base) and empirically plausible elasticities, pilot simulations indicate: on-time material delivery +14–22 percentage points, logistics cost −10–18%, contractor price inflation −4–7%, city-region GDP +1.1–2.6%, and net in-migration to upgraded hubs +1.2–2.9% over 10 years; unmanaged work-zone congestion raises project durations +6–11% and wage drift +3–5% in tight labor markets. Compared with speed-only models, adding reliability cuts late-penalty exposure −25–40% and improves welfare gains +0.2–0.5 pp. The framework produces sequencing recommendations (which link first, when) and procurement guidance (lane-closure policies, night work, staging) that jointly maximize welfare and project NPV under labor and supply-chain constraints.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112268"},"PeriodicalIF":11.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079555","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

Causal analysis of ship inspection data and maritime accidents through causal neural networks 基于因果神经网络的船舶检验数据与海上事故的因果分析

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-07-01 Epub Date: 2026-01-21 DOI: 10.1016/j.ress.2026.112255

Run Liu , Mingyang Zhang , Ran Yan , Zaili Yang

{"title":"Causal analysis of ship inspection data and maritime accidents through causal neural networks","authors":"Run Liu , Mingyang Zhang , Ran Yan , Zaili Yang","doi":"10.1016/j.ress.2026.112255","DOIUrl":"10.1016/j.ress.2026.112255","url":null,"abstract":"<div><div>Maritime transportation serves as the backbone of international trade, yet its growth is accompanied by an increasing number of maritime accidents. Therefore, reducing maritime accidents and mitigating their impact are crucial to supporting sustainable maritime industry development. Port state control (PSC) inspection is a key regulatory tool for enhancing maritime safety by inspecting foreign visiting ships to port. However, the actual impact of PSC inspection on reducing maritime accidents and how it can be further optimised remains unclear. This study employs a causal generative neural network model to explore the causal relationship among ship particulars, historical PSC inspection results, and future maritime accidents through a directed acyclic graph (DAG). By integrating causal discovery and causal inference, this study provides a robust framework for modelling maritime accident causation and overcomes limitations of traditional methods that often rely on discretisation or linear assumptions. Based on the identified optimal causal DAG, this study conducts decile-level causal intervention analyses on key contributing variables to quantify their effects on both the occurrence and severity of various types of maritime accidents. The results indicate that the optimal maximum PSC inspection interval is 409–509 days, which is consistent with the existing longest inspection time window (10–18 months) in the Tokyo Memorandum of Understanding. The probability and severity of maritime accidents exhibit a U-shaped relationship with the mean inspection interval, and the lowest risk occurs at 170–210 days. Additionally, the life-saving equipment conditions should be paid more attention in the PSC inspection. Due to the quantitative analysis nature, the new method will be able to help analyse the quantity of the increased or decreased risks against different types of maritime accidents, supporting a comprehensive perspective for safety management and accident prevention strategies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112255"},"PeriodicalIF":11.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079549","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

Analyzing power network vulnerability considering spatial heterogeneous demand under extreme heat 考虑空间异质需求的极端高温条件下电网脆弱性分析

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-07-01 Epub Date: 2026-01-24 DOI: 10.1016/j.ress.2026.112287

Mijie Du , Peng Guo , Jing Zhao , Enrico Zio

{"title":"Analyzing power network vulnerability considering spatial heterogeneous demand under extreme heat","authors":"Mijie Du , Peng Guo , Jing Zhao , Enrico Zio","doi":"10.1016/j.ress.2026.112287","DOIUrl":"10.1016/j.ress.2026.112287","url":null,"abstract":"<div><div>Power networks are facing significant challenges from frequent extreme heat in terms of operational stress and the risk of cascading failures. This paper proposes a vulnerability analysis framework for power networks exposed to extreme heat scenarios, taking into account spatial heterogeneous demand. Based on the complex distribution of power demand and the varying social impacts of service disruptions, we introduce the Service Disruption–Social Vulnerability Index (SD-SVI) to build a spatial demand model at the level of urban functional zones. Using power network and cascading failure modeling, we apply the SD-SVI weighted method to assess the network’s vulnerability. In addition, we model load growth and line failure rates as driven by extreme heat and design a dual-objective optimization model that considers both vulnerability and failure probability. Case study results show that the SD-SVI weighting significantly affects the vulnerability of the power network, with the continued temperature increase due to climate change making the network more vulnerable. Furthermore, analysis of the obtained Pareto front solutions reveals that extreme heat has a nonlinear effect on system vulnerability, and when temperatures exceed 36°C, single or double branch failures dominate the Pareto front of the power network. Based on the “average frequency × inferred vulnerability” composite index, our analysis shows that protecting vulnerable lines can greatly improve network performance and its ability to adapt to multiple extreme heat scenarios. This study provides theoretical insights and practical guidance for power network vulnerability analysis, risk management and climate change adaptation.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112287"},"PeriodicalIF":11.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079652","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

Effects of human performance on ship collision risk in restricted waters: A Bayesian network driven by real navigation data 人类行为对受限水域船舶碰撞风险的影响：基于真实导航数据驱动的贝叶斯网络

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-07-01 Epub Date: 2026-01-23 DOI: 10.1016/j.ress.2026.112280

Jiaxin Du , Yongtao Xi , Jinxian Weng , Bing Han , Haifeng Ding

{"title":"Effects of human performance on ship collision risk in restricted waters: A Bayesian network driven by real navigation data","authors":"Jiaxin Du , Yongtao Xi , Jinxian Weng , Bing Han , Haifeng Ding","doi":"10.1016/j.ress.2026.112280","DOIUrl":"10.1016/j.ress.2026.112280","url":null,"abstract":"<div><div>Ensuring navigation safety is a key objective in maritime transport, particularly in restricted waters where human factors contribute predominantly to accidents. Complex operating conditions increase crew workload, induce physiological responses, and lead to ship behavioral changes that shape collision risk. Based on the Information-Decision-Action (IDA) theory, this study develops an Environment-Human state-Ship behavior-Consequence (EHSC) framework and constructs a real navigation data-driven Bayesian Network (BN). Real-world experiments on the Huangpu River were designed to investigate how environmental conditions influence seafarers’ states, which further affect ship behavior and risk. Results indicate that the minimum distance to other vessels and speed are the most sensitive determinants of collision risk. Low Galvanic Skin Response (GSR), which tends to occur under nighttime conditions, limited traffic interactions, or low traffic density, is associated with close-proximity navigation and sustained high speed. Captains aged 50–60 exhibit stronger risk management capabilities. These findings clarify human-performance pathways of collision risk and provide valuable support for early warning systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112280"},"PeriodicalIF":11.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079653","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

Distributionally robust fairness-based last-mile relief network optimization with casualty uncertainty 考虑人员伤亡不确定性的分布式鲁棒公平性最后一英里救助网络优化

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-07-01 Epub Date: 2026-01-28 DOI: 10.1016/j.ress.2026.112305

Guoqing Yang, Hongye Yuan, Wenshuai Yang, Ruru Jia

{"title":"Distributionally robust fairness-based last-mile relief network optimization with casualty uncertainty","authors":"Guoqing Yang, Hongye Yuan, Wenshuai Yang, Ruru Jia","doi":"10.1016/j.ress.2026.112305","DOIUrl":"10.1016/j.ress.2026.112305","url":null,"abstract":"<div><div>The suddenness and the casualties’ uncertainty of natural disasters urgently require a fair and robust network design for the medical supply distribution and the injured evacuation to reduce their post-disaster impact. This study establishes a distributionally robust chance-constrained model for medical supplies allocation in last-mile relief networks, with the objective of minimizing the worst-case Conditional Value-at-Risk (CVaR) of supply shortages. The distribution of severely injured casualties is characterized via a scenario-wise ambiguity set, thereby the proposed model is reformulated as a mixed-integer linear programming problem for tractability. Numerical experiment based on Wenchuan earthquake derives several important findings. First, total supplies and raw materials exhibit analogous effects—increasing either reduces shortage levels initially, but further reductions are constrained by the other factor; Second, in response to high risks, the tendency is to build additional medical stations rather than expanding the scale of existing ones to disperse risk. Conversely, when risk is low, scaling up existing medical stations is preferred over establishing temporary facilities; Finally, under out-of-sample data fluctuations, the CVaR model demonstrates stronger robustness than the sample average approximation model, with consistently smaller standard deviations and superior stability.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112305"},"PeriodicalIF":11.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079654","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

Line importance sampling for reliability analysis with complex failure domain 复杂失效域可靠性分析的线路重要性抽样

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-07-01 Epub Date: 2026-01-26 DOI: 10.1016/j.ress.2026.112296

Xiaobo Zhang

{"title":"Line importance sampling for reliability analysis with complex failure domain","authors":"Xiaobo Zhang","doi":"10.1016/j.ress.2026.112296","DOIUrl":"10.1016/j.ress.2026.112296","url":null,"abstract":"<div><div>Reliability analysis is essential for evaluating the safety of engineering systems under uncertainty. However, modern structural systems often involve highly nonlinear performance functions and complex failure domains, making efficient reliability estimation challenging. Line Sampling (LS) improves efficiency by transforming a high-dimensional problem into a series of conditional one-dimensional problems along a predefined important direction, but it performs well only for weakly/mildly nonlinear cases and depends heavily on the important direction. To overcome these limitations, a novel Line Importance Sampling (LIS) framework is proposed by integrating the efficiency of LS with the flexibility of importance sampling. In LIS, sampling is conducted in a rotated (n-1)-dimensional space, where the theoretically optimal importance sampling density is derived. Furthermore, an adaptive Cross-Entropy-based LIS (CE-LIS) is developed by minimizing the Kullback–Leibler divergence between the optimal and assumed sampling densities, with analytical update rules derived for Gaussian Mixture model and von Mises–Fisher–Nakagami Mixture model. This mechanism adaptively concentrates samples around dominant failure regions even when the initial direction is suboptimal. Applications to five problems with multiple design points or non-convex limit-state surfaces demonstrate that the proposed CE-LIS achieves high accuracy and robustness, effectively overcoming the inherent limitations of the original LS method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112296"},"PeriodicalIF":11.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189803","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