Reliability Engineering & System Safety最新文献

{"title":"Type-Ⅰ censored reliability qualification test design for weibull products based on expert judgments","authors":"Yunlei Tan ,&nbsp;Ping Jiang ,&nbsp;Yunyan Xing ,&nbsp;Jianjun Qi","doi":"10.1016/j.ress.2025.111074","DOIUrl":"10.1016/j.ress.2025.111074","url":null,"abstract":"<div><div>Reliability Qualification Testing (RQT) is crucial for confirming that a product complies with specified reliability standards. Traditionally, practitioners design a lifetime test plan by referring to the GJB899A-2009 \"Reliability Qualification and Acceptance Testing\", which provides test plans subjected to the test specifications and risks. However, these plans often demand lengthy testing periods and are not practical in many scenarios where conclusions need to be provided in a short term. In this paper, a framework of test plan design with a derivation method for risk calculation is proposed for Weibull-distributed products. In this framework, to estimate the probability density distribution of product lifetime <em>T</em> with multiple expert judgments, a nonlinear optimization problem is modeled, and imprecise prior information is converted to combinatorial constraints. To search for parameters' approximate optimal solution, PSO-MEM is constructed, which is a framework integrating the particle swarm optimization (PSO) with the maximum entropy principle (MEM). What's more, to cover a relatively comprehensive range of test specifications and types, equations are modified under three typical distributions and two kinds of lifetime test plans. Besides, the proposed method is compared with authoritative standards like GJB 899A-2009 under different test conditions. The results show that test plans searched in this paper yield lower risk value than standard plans, which highlights the effectiveness of the new framework in deriving RQT plans under multiple imprecise information and different test modes.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"262 ","pages":"Article 111074"},"PeriodicalIF":9.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879037","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":"UQ state-dependent framework for seismic fragility assessment of industrial components","authors":"Chiara Nardin ,&nbsp;Stefano Marelli ,&nbsp;Oreste S. Bursi ,&nbsp;Bruno Sudret ,&nbsp;Marco Broccardo","doi":"10.1016/j.ress.2025.111067","DOIUrl":"10.1016/j.ress.2025.111067","url":null,"abstract":"<div><div>Recently, there has been increased interest in assessing the seismic fragility of industrial plants and process equipment. This is reflected in the growing number of studies, community-funded research projects and experimental campaigns on the matter. Nonetheless, the complexity of the problem and its inherent modelling, coupled with a general scarcity of available data on process equipment, has limited the development of risk assessment methods. In fact, these limitations have led to the creation of simplified and quick-to-run models. In this context, we propose an innovative framework for developing state-dependent fragility functions. This new methodology combines limited data with the power of metamodelling and statistical techniques, namely polynomial chaos expansions (PCE) and bootstrapping. Therefore, we validated the framework on a simplified and computationally efficient MDoF system endowed with Bouc–Wen hysteresis. Then, we tested it on a real nonstructural industrial process component. Specifically, we applied the state-dependent fragility framework to a critical vertical tank of a multicomponent full-scale 3D steel braced frame (BF). The seismic performance of the BF endowed with process components was captured by means of shake table campaign within the European SPIF project. Finally, we derived state-dependent fragility functions based on the combination of PCE and bootstrap at a greatly reduced computational cost.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111067"},"PeriodicalIF":9.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870268","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":"Probabilistic resilience assessment of urban distribution power grids by fast inference of multi-source multi-terminal network reliability","authors":"Yunqi Yan ,&nbsp;Ying Chen ,&nbsp;Zhengda Cui ,&nbsp;Tannan Xiao","doi":"10.1016/j.ress.2025.111077","DOIUrl":"10.1016/j.ress.2025.111077","url":null,"abstract":"<div><div>Urban power distribution grids featuring loopy topologies and integrated distributed generations pose significant challenges for efficient and precise resilience quantification against disruptive events. This paper presents a probabilistic resilience assessment framework tailored for such grids. Risk metrics grounded in loss of load probability (LOLP) and expected energy not served (EENS) are formulated to evaluate resilience across multiple temporal stages. A multi-source multi-terminal network reliability (MSMT-NR) modeling approach is proposed to characterize the stochastic impact of component failures on load point connectivity. A computationally efficient algorithm framework is developed for the inference of the MSMT-NR problem, comprising: (1) Derivation of analytical LOLP expressions for grid topologies exhibiting tree-like load subgraphs; (2) A deletion–contraction decomposition technique generating solvable tree subgraphs from arbitrary network structures; (3) A computational graph-based inference methodology enabling efficient MSMT-NR evaluation and automatic differentiation for sensitivity analysis of component importance measures. Strategies for enhancing scalability to large-scale grids are devised. Extensive case studies on a real-world 30,894-node distribution grid corroborate the efficiency and precision of the proposed approach.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111077"},"PeriodicalIF":9.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855711","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":"Optimal intensity measure and probabilistic seismic demand model for the assessment of historical masonry buildings: application to two case studies","authors":"Daniel Caicedo ,&nbsp;Igor Tomić ,&nbsp;Shaghayegh Karimzadeh ,&nbsp;Vasco Bernardo ,&nbsp;Katrin Beyer ,&nbsp;Paulo B. Lourenço","doi":"10.1016/j.ress.2025.111149","DOIUrl":"10.1016/j.ress.2025.111149","url":null,"abstract":"<div><div>This paper presents a probabilistic seismic demand model (PSDM) as a relationship between intensity measures (IMs) and engineering demand parameters (EDPs) for the seismic assessment of two case studies resembling historical masonry buildings. The first one is representative of stiff monumental buildings, and the second of tall and slender masonry buildings. Both structures are modelled in the OpenSees software using three-dimensional macroelements that consider both the in-plane and out-of-plane response of masonry walls. A set of 100 accelerograms are selected to represent the seismic excitation. After full characterization of the seismic input in terms of IMs, both buildings are subjected to the action of these accelerograms to study the maximum structural response in the context of cloud analysis. The most suitable IMs are determined subsequently under the notions of efficiency, practicability, proficiency, and sufficiency. In addition, a composed measure is proposed as a linear combination in logarithmic space of the IMs that exhibit the best coefficient of determination (<em>R</em>²) within the EDP vs. IM regression. This optimal composed measure is determined through machine learning-based Lasso regression. In the final stage of the study, fragility curves are derived to measure the likelihood of exceedance of certain levels of average roof displacement in terms of IM parameters.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111149"},"PeriodicalIF":9.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863581","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":"Modeling multivariate degradation data with dynamic covariates under a Bayesian framework","authors":"Zhengzhi Lin ,&nbsp;Xiao Liu ,&nbsp;Yisha Xiang ,&nbsp;Yili Hong","doi":"10.1016/j.ress.2025.111115","DOIUrl":"10.1016/j.ress.2025.111115","url":null,"abstract":"<div><div>Degradation data are essential for determining the reliability of high-end products and systems, especially when covering multiple degradation characteristics (DCs). Modern degradation studies not only measure these characteristics but also record dynamic system usage and environmental factors, such as temperature, humidity, and ultraviolet exposures, referred to as the dynamic covariates. Most current research either focuses on a single DC with dynamic covariates or multiple DCs with fixed covariates. This paper presents a Bayesian framework to analyze data with multiple DCs, which incorporates dynamic covariates. We develop a Bayesian framework for mixed effect nonlinear general path models to describe the degradation path and use Bayesian shape-constrained P-splines to model the effects of dynamic covariates. We also detail algorithms for estimating the failure time distribution induced by our degradation model, validate the developed methods through simulation, and illustrate their use in predicting the lifespan of organic coatings in dynamic environments.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111115"},"PeriodicalIF":9.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863584","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":"Multi-timescale risk-averse restoration for interdependent water–power networks with joint reconfiguration and diverse uncertainties","authors":"Yesen Yang ,&nbsp;Zhengmao Li ,&nbsp;Edmond Y. Lo","doi":"10.1016/j.ress.2025.111083","DOIUrl":"10.1016/j.ress.2025.111083","url":null,"abstract":"<div><div>The growing interdependencies between water and power systems have increased the risk of cascading disruptions and widespread blackouts. Such interdependencies, together with different operational characteristics and multiple uncertainties, introduce additional complexities to service restoration. To address these issues, this paper proposes a coordinated multi-timescale restoration strategy for interdependent water–power networks (IWPNs). First, we model the IWPN as network-based with physical mechanisms, incorporating component-wise interdependencies and varying consumer demands. Features comprising pipe damage (water leakage) and storage as well as renewable generations are modelled to better reflect restoration better. Specifically, the joint reconfigurability of water and power networks is first applied for adjustment of topologies and leverages off backup components by coordinated setting of valves and switches. Then, an updated estimation for multiple uncertainties during restoration is utilized, which offers increasing clarity to support better decision-making. These uncertainties arise from renewable generations and water and power demands. A multi-timescale decision framework is developed to capture these effects and tune restoration measures based on response speeds to facilitate efficient and reliable restoration. Finally, the method is implemented by combining robust optimization and risk-averse stochastic programming and applied to a community-scale test system with 25 water nodes and 33 power buses. The proposed method is compared with five conventional methods with numerical results demonstrating the improvements arising from an interdependent restoration, joint reconfigurability, and multi-timescale optimizations.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111083"},"PeriodicalIF":9.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859866","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":"Resilience modeling of transportation infrastructure and network based on the semi-Markov process considering resource dependency","authors":"Rui Ma,&nbsp;Huihui Dong,&nbsp;Qiang Han,&nbsp;Xiuli Du","doi":"10.1016/j.ress.2025.111159","DOIUrl":"10.1016/j.ress.2025.111159","url":null,"abstract":"<div><div>Resilience quantification for transportation systems remains challenging due to complex interdependencies between infrastructure degradation and network functional evolution. This study presents probabilistic resilience models of transportation infrastructures and networks, which integrate resource-dependent semi-Markov processes with cascading failure mechanisms to address this problem. Specifically, unlike conventional models treating physical and functional failures in isolation, the proposed models explicitly couple infrastructure-level fragility with network-level traffic redistribution dynamics by cascading failure modeling. With respect to the functional recovery modeling, the core innovation of this study lies in the modified semi-Markov recovery model, which integrates the semi-Markov process and the Bayesian-updated resource dependency model to address both recovery strategy selection and the effect of the available recovery resource on the recovery time distribution. Further, the procedure and simulation-based algorithm of the resilience models are provided. A case study is then carried out for a real-world transportation network to illustrate the applicability of the proposed resilience models. Case analysis results demonstrate that recovery strategy selection drives 84% variability in infrastructure-level resilience and 74% divergence in network-wide resilience metrics, while severe resource constraints degrade infrastructure-level resilience by 71% compared to optimal availability. Crucially, conventional criticality-based allocation prolongs network recovery efficiency by 50% versus functionality-loss-prioritized strategies. It indicates the necessity of multi-criteria including functionality loss severity, topological importance, and on-site construction limitations. Methodologically, it unifies infrastructure physics failure with network flow dynamics evolution for urban transport decision-making. The framework enables adaptive recovery via real-time capacity predictions and resource-strategy optimization, providing stakeholders with actionable insights.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111159"},"PeriodicalIF":9.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859868","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":"Data-driven reliability evolution prediction of underground pipeline under corrosion","authors":"Hao Shen ,&nbsp;Yihuan Wang ,&nbsp;Wei Liu ,&nbsp;Siming Liu ,&nbsp;Guojin Qin","doi":"10.1016/j.ress.2025.111148","DOIUrl":"10.1016/j.ress.2025.111148","url":null,"abstract":"<div><div>Corrosion presents a substantial threat to both the structural integrity and the service life of pipelines. Despite the availability of existing models for assessing corrosion rate and pipeline reliability in the oil and gas industry, their applicability is constrained by the inherent complexity of the surrounding soil environment. In this study, a novel artificial intelligence-based hybrid model was developed to predict pipeline corrosion rate. The Extreme Learning Machine (ELM) was employed as the primary predictor. The Bald Eagle Search (BES) algorithm was integrated and enhanced by incorporating Lévy flight search and Simulated annealing (SA) algorithms, forming the LSBES algorithm to optimize the parameter learning of the ELM model. Three machine learning models were developed as benchmarks to evaluate the performance of the proposed hybrid model. The results demonstrate that the LSBES-ELM model demonstrates superior predictive accuracy and stability, with a mAP of approaching 95 % and a RE ranging from 0.0274 to 0.0761, surpassing the performance of both baseline ELM-based models (BES-ELM, ELM) and the non-optimized BP Neural Network (BPNN). Furthermore, the LSBES-ELM-MCS model was developed with the LSBES-ELM model and Monte Carlo simulation (MCS) to perform a dynamic assessment of the optimal distribution of factors influencing corrosion rates and the reliability evolution prediction of pipelines with various buried soil conditions. With target reliability, the optimal inspection interval for the case pipeline was projected to fall between 21 and 24 years. This study is expected to present significance for modeling corroded pipeline reliability and contribute to the broader goal of enhancing pipeline safety and longevity in the oil and gas industry.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111148"},"PeriodicalIF":9.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834960","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 methodology for data-driven risk analysis based on virtual-reality-generated information and generative adversarial network","authors":"Huixing Meng ,&nbsp;Jialei Liao ,&nbsp;Jiali Liang ,&nbsp;Xiuquan Liu","doi":"10.1016/j.ress.2025.111157","DOIUrl":"10.1016/j.ress.2025.111157","url":null,"abstract":"<div><div>To improve the safety of complex systems, it is essential to analyze and maintain the risk at an acceptable level. However, risk analysis is usually encountered with the difficulty of data deficiency, particularly for complex systems and unusual operations. In this paper, we proposed a methodology for data-driven risk analysis based on virtual-reality-generated information and a generative adversarial network (GAN). First, the concerned accident scenario for risk analysis is formulated. Second, the virtual reality (VR) model of the corresponding accident scenarios and operations is constructed. The experiment data, containing operation failure information, is subsequently collected. Third, to effectively support the data-driven risk analysis, the scale of the experiment data is augmented through GAN. Based on the augmented data, risk analysis is carried out in the form of data-driven Bayesian networks (BN). Eventually, the feasibility of the proposed methodology is validated with the case study of risk analysis of emergency operations in deepwater blowout. Our results show that the proposed methodology is beneficial to deal with the data deficiency in the domain of risk analysis.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111157"},"PeriodicalIF":9.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863585","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 condition-based maintenance for shock systems based on damage evolutions using deep reinforcement learning","authors":"Yudao Sun ,&nbsp;Juan Yin","doi":"10.1016/j.ress.2025.111095","DOIUrl":"10.1016/j.ress.2025.111095","url":null,"abstract":"<div><div>In the industry domain, maintenance tasks and resources need to be allocated to industrial systems to avoid unplanned downtime. We explore the dynamic condition-based maintenance strategy for systems comprising multiple components, in which each component undergoes external shocks along with time and is maintained individually. For each component, random shocks arrive following a homogeneous Poisson process, and the evolution of the component’s state is characterized using a Markov process. The dynamic condition-based maintenance policy for the developed shock system, depicted as a Markov decision process, is introduced. To minimize the overall system cost, the maintenance optimization problem is discussed to determine the most cost-effective maintenance actions. A tailored advantage actor-critic algorithm in deep reinforcement learning is proposed to address the challenge of high dimensionality. Finally, numerical examples demonstrate the efficiency of the proposed method in searching for optimal maintenance actions and reducing maintenance costs.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111095"},"PeriodicalIF":9.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844746","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}