Reliability Engineering & System Safety最新文献

{"title":"Analysis of RUL dynamics and uncertainty via time transformation","authors":"Pierre Dersin ,&nbsp;Roberto Rocchetta","doi":"10.1016/j.ress.2025.111730","DOIUrl":"10.1016/j.ress.2025.111730","url":null,"abstract":"<div><div>This work introduces a novel analytical method to analyze the dynamics of remaining useful life (RUL) and quantify uncertainty in its estimation. The approach employs a time transformation that makes the mean residual life (MRL) a linear function of transformed time, enabling the derivation of explicit RUL confidence bounds. Once mapped back to physical space, the bounds quantify aleatoric (stochastic) uncertainty in RUL and yield asymmetrical confidence intervals for both parametric and non-parametric lifetime distributions. The approach leverages a key feature of reliability distributions: the average RUL loss rate, <span><math><mi>k</mi></math></span>, in transformed time, facilitating a direct derivation of confidence bounds. In parametric cases, <span><math><mi>k</mi></math></span> is uniquely defined by the reliability distribution parameters, while for non-parametric distributions, it is derived from data by estimating the coefficient of variation. Higher slopes indicate faster degradation, leading to narrower confidence intervals and lower RUL variance. The method’s applicability to stochastic processes and robustness under different data volumes are also investigated and discussed. The novel approach reveals heretofore unknown insights into classical reliability distributions. It is demonstrated through real-world applications, including LED reliability assessment, parallel system RUL estimation, and turbofan lifespan prediction using NASA N-CMAPSS data, offering a new perspective on the evolving dynamics of mean residual life and remaining useful life.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111730"},"PeriodicalIF":11.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221130","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 recursive algorithm for reliability evaluation of multi-state hierarchical systems with stochastic dependent components","authors":"Chen Jiang ,&nbsp;Muxia Sun ,&nbsp;Luyao Wang ,&nbsp;Zisheng Wang ,&nbsp;Yan-Fu Li","doi":"10.1016/j.ress.2025.111653","DOIUrl":"10.1016/j.ress.2025.111653","url":null,"abstract":"<div><div>Multi-state hierarchical systems (MSHSs), composed of recursively nested subsystems, are prevalent in engineering applications. However, their reliability evaluation remains challenging, especially when components exhibit stochastic dependencies. Existing methods either assume mutual independence – which oversimplifies real-world systems – or suffer from high computational cost and limited structural generality. In this work, we propose a computationally efficient recursive algorithm based on Bayesian Networks (BNs) for evaluating the reliability of generalized MSHSs with dependent components. Unlike traditional methods that rely on global system representations, our approach leverages the system’s hierarchical architecture by assigning a local BN to each structural level, thereby capturing intra-level dependencies while maintaining scalable computation. The algorithm proceeds in a bottom-up manner to iteratively compute marginal and conditional state distributions, ultimately yielding the system-level reliability. The method, to our knowledge, offers the fastest known performance for MSHSs with stochastic dependence. Numerical experiments and two case studies demonstrate that the proposed algorithm reduces computation time by up to 95% compared to the Universal Generating Function (UGF) and Multivalued Decision Diagram (MDD) approaches, and by up to 99.5% compared to the Monte Carlo Simulation (MCS) method, particularly in systems with inter-subsystem dependence. These results highlight the proposed method’s strong generality, structural adaptability, and significant computational advantage in complex reliability modeling.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111653"},"PeriodicalIF":11.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267745","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":"Algorithmic analysis of a complex reliability system subject to multiple events with a preventive maintenance strategy and a Bernoulli vacation policy through MMAPs","authors":"Juan Eloy Ruiz-Castro ,&nbsp;Hugo Alaín Zapata-Ceballos","doi":"10.1016/j.ress.2025.111744","DOIUrl":"10.1016/j.ress.2025.111744","url":null,"abstract":"<div><div>In this work, a single-unit multi-state system is considered. The system is subject to internal failures, as well as external shocks with multiple consequences. It also incorporates a preventive maintenance strategy and a Bernoulli vacation policy for the repairperson. It is algorithmically modeled in both continuous and discrete time using Marked Markovian Arrival Processes (MMAP). The system's operation/degradation level is divided into an indeterminate number of levels. Upon returning from a vacation period, the repair technician may initiate corrective repair, perform preventive maintenance, replace the unit, remain idle at the workplace, or begin a new vacation period. The decision in the latter two cases is made probabilistically based on the system's operational level. This methodology allows the model and its associated measures to be algorithmically derived in both transient and stationary regimes, presented in a matrix-algorithmic form. Analytical-matrix methods are used to obtain the system's steady-state behaviour as well as various performance measures. Costs and rewards are introduced to analyze when the system becomes profitable. Measures associated with costs over time and in the stationary regime are defined and considered for optimization studies. A numerical example demonstrates the versatility of the model by solving a probabilistic optimization problem using a multi-objective Pareto analysis approach and performing a comparative evaluation of multiple models. Genetic algorithm is applied to find the optimization results in the reduced solution space. All modeling and associated measures have been computationally implemented in Matlab.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111744"},"PeriodicalIF":11.0,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158540","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":"Time-variant reliability analysis via advanced most probable point trajectory tracking","authors":"Enyong Zhao ,&nbsp;Qihan Wang ,&nbsp;Shuangkai Hou ,&nbsp;Zhen Luo ,&nbsp;Wei Gao","doi":"10.1016/j.ress.2025.111748","DOIUrl":"10.1016/j.ress.2025.111748","url":null,"abstract":"<div><div>Structural reliability evolves due to environmental conditions and varying loads, leading to gradual structural deterioration. Accurately capturing this time-variant behavior is essential for assessing failure probability over a specified time horizon. This study proposes an adaptive virtual model-assisted most probable point trajectory-based (AdaVM-MPPT) approach for time-variant reliability analysis under stochastic loadings, focusing on the trajectory tracking of the most probable point (MPP). A stochastic process discretization technique is adopted to decompose the time-variant limit state function in the time domain. To enhance computational efficiency and accuracy, the Extended Support Vector Regression (X-SVR) is utilized for virtual model construction. The virtual model approximates the relationship between the structural uncertainty inputs, including geometries, material properties, degradation processes, applied loading conditions, and the limit state hyperplane. Therefore, a two-stage adaptive sampling strategy is developed to effectively establish the virtual model and capture the MPP at all discretized time instants. The identified MPPs are then used to approximate the most probable point trajectory (MPPT), enabling continuous prediction at any time point within the specified period. The proposed framework consistently generates MPPs over the specified time period based on the MPPT, allowing for efficient computation of time-variant reliability using the multivariate normal distribution. The proposed AdaVM-MPPT method for time-variant reliability analysis offers several advantages. The X-SVR algorithm and two-stage adaptive sampling strategy improve the MPP capturing efficiency significantly. Furthermore, the computational cost of time-variant reliability analysis associated with the stochastic process discretization size can be significantly reduced based on the availability of MPPT. These two advancements significantly improve the efficiency of traditional time-variant reliability analysis methods. Finally, the applicability and computational efficiency of the proposed method are fully demonstrated through a test function and practice-stimulated engineering problems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111748"},"PeriodicalIF":11.0,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158511","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":"Robust seismic response evaluation considering an uncertainty emerging in asymmetric bridges","authors":"Yu Lin ,&nbsp;Yuguang Fu ,&nbsp;Jubo Sun ,&nbsp;Ruihong Xie ,&nbsp;Xinhao He","doi":"10.1016/j.ress.2025.111740","DOIUrl":"10.1016/j.ress.2025.111740","url":null,"abstract":"<div><div>The robust seismic design of structural systems relies on accurately assessing peak seismic responses amidst various uncertainties. While prior studies have focused on incident direction and motion-to-motion variability of ground motions, this study identifies and characterizes a previously unrecognized uncertainty termed the Uncertainty in Asymmetric Structures (UAS) that can significantly influence seismic performance assessment. The UAS effect manifests as a systematic difference in the most critical seismic response, over all incident directions, between an asymmetric structure and its horizontal-plane mirror image. Through analytical derivation under linear-elastic assumptions, the UAS effect is shown to originate from the coupled influence of structural asymmetry, quantified by the Cross-Structural Term (CST), and bidirectional ground motion characteristics, quantified by the Cross-Modal Response (CMR). To evaluate its engineering implications, nonlinear time-history analyses are performed on five finite element models of multi-span continuous curved girder bridges with varying degrees of asymmetry. Spectrum-compatible ground motions are employed to capture motion-to-motion variability, while synthesized bidirectional ground motions are used to explore the influence of directionality. Results confirm the theoretical predictions, revealing that the UAS effect can alter critical seismic responses by up to 20 %, with its prominence increasing under greater structural asymmetry or reduced ground motion directionality. These findings introduce a new dimension of seismic input–structure interaction that has direct implications for performance-based and risk-informed seismic design.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111740"},"PeriodicalIF":11.0,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158414","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 sampling-variability-free dimension-reduced probability density evolution equation method for high-dimensional nonlinear stochastic dynamic analysis","authors":"Yang Zhang ,&nbsp;Meng-Ze Lyu ,&nbsp;Jun Xu ,&nbsp;Yi Luo","doi":"10.1016/j.ress.2025.111727","DOIUrl":"10.1016/j.ress.2025.111727","url":null,"abstract":"<div><div>Effective stochastic dynamic analysis of high-dimensional nonlinear structural systems is essential for ensuring structural safety. However, two key challenges persist: (1) accurately capturing physical system response characteristics with limited samples while avoiding sampling-induced variability, and (2) effectively extracting probabilistic information from stochastic response samples. To address these issues, a sampling-variability-free Dimension-Reduced Probability Density Evolution Equation (DR-PDEE) method is proposed by integrating a deterministic sampling method, i.e., the New Generating Vectors-based Number-Theoretic Method (NGV-NTM), with the DR-PDEE framework. In this method, the NGV-NTM is first performed to efficiently generate deterministic high-dimensional point sets with excellent space-filling property, enabling variability-free representation of stochastic input samples. These samples are used to compute dynamic responses, from which intrinsic drift functions are subsequently estimated, as required by the DR-PDEE. The DR-PDEE method then yields one- or two-dimensional partial differential equations governing the evolution of the response probability density function, which can be solved for effective stochastic dynamic response analysis. In this work, the theoretical foundations of the proposed method are first established via number theory and the Kramers–Moyal expansion, followed by a three-step numerical implementation strategy. Numerical examples demonstrate that the proposed method achieves superior accuracy and efficiency while eliminating sampling variability, outperforming both the Monte Carlo simulation and the random-sampling-based DR-PDEE solution.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111727"},"PeriodicalIF":11.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109305","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-level leakage risk management integrated framework for urban water distribution network","authors":"Xixun Huang ,&nbsp;Liang Li ,&nbsp;Yang Yu ,&nbsp;Junfeng Diao","doi":"10.1016/j.ress.2025.111723","DOIUrl":"10.1016/j.ress.2025.111723","url":null,"abstract":"<div><div>Global WDN are universally challenged by water leakage and aging, necessitating the adoption of scientific O&amp;M strategies to mitigate risks and ensure reliable operation. Existing research has focused solely on a single dimension and ignored the hierarchical characteristics of WDNs. Traditional models have limitations regarding adaptability to time-series and unbalanced data processing. This study integrates pipe structure characteristics and risk management theory to propose a multi-level leakage risk management framework, dividing the WDN into two levels: main pipes and DMAs. For predicting main pipe failures, the study compares survival analysis, machine learning, and their integrated models, innovatively adopting the integrated model XGBSE to optimize temporal risk modeling. The GADW-RTOPSIS dynamic decision-making method is proposed for DMA risk assessment, dynamically adjusting subjective and objective weights to adapt to data uncertainty and enhance assessment reliability. Tests based on Dongguan City’s WDN show that the XGBSE achieves a C-index of 0.9583 in main pipe failure prediction tasks, significantly outperforming traditional models. The GADW-RTOPSIS demonstrates powerful high-risk sorting capabilities and economic benefits 3.34 times greater than the overall average when guiding DMA renovations. The research findings provide a refined risk-driven decision-making paradigm for urban WDN, supporting the efficient allocation of pipe O&amp;M resources.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111723"},"PeriodicalIF":11.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120910","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":"An edge load cascading failure model and vulnerability analysis of coupled critical infrastructure networks: Considering functional and geographical interdependency","authors":"Jing Wang ,&nbsp;Yuhui Huang","doi":"10.1016/j.ress.2025.111719","DOIUrl":"10.1016/j.ress.2025.111719","url":null,"abstract":"<div><div>Infrastructure networks are not isolated, but rather highly interconnected and interdependent. The flow characteristics of loads within the network and the existence of interdependencies between networks can lead to cascading failures throughout a region or country when one component is disrupted. The general focus is on analyzing infrastructure networks from the intra-layer structural perspective; it is less understood the coupling mode of interdependency between layers and the loads on the network. This paper proposes a new cascading failure model based on edge load, taking into account both intra-layer network structure and inter-layer functional and geographical interdependencies, while also explores the impacts of different factors on the cascading failures. Results show that nodes with strong dependencies, especially parent-dependency, play vital roles in supporting coupled networks. It compares the vulnerability differences of the system under different attack strategies based on intra-layer structure and inter-layer correlation, finding the outcome changes with functional interdependency strength and geographical distance. This study assesses relevant factors affecting the vulnerability of interdependent infrastructure networks from a modeling perspective. Meanwhile, compared with the traditional model, the new model can better resist the spread risk of cascading failures, thus providing valuable lessons for guiding realistic system construction.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111719"},"PeriodicalIF":11.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221136","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":"Enhancing the survivability of a new data-driven robust airline hub network with risk-averse criterion","authors":"Meiyu Liu ,&nbsp;Naiqi Liu ,&nbsp;Shanshan Gao","doi":"10.1016/j.ress.2025.111711","DOIUrl":"10.1016/j.ress.2025.111711","url":null,"abstract":"<div><div>The widely adopted hub-and-spoke architecture in airline network designs can trigger cascading effects during disruptions and result in further losses. This paper aims to enhance the survivability of airline hub networks in the design phase by optimizing reliability, sustainability, and efficiency. However, it is challenging to account for reliability under unpredictable disruptions such as interdiction and natural disasters. Under the stimulation of available event information, it is a promising solution to incorporate uncertain disruption scenarios into reliable airline hub network design through a data-driven robust approach. This paper develops a bi-level multi-objective optimization framework, and builds new risk-neutral and risk-averse models under the worst-case mean and conditional-value-at-risk criteria, where data-driven ambiguity sets are constructed through statistical hypothesis testing, and empirical probability distribution is determined by fault tree analysis. The constructed ambiguity sets have probabilistic guarantee, which help us transform the proposed models into mixed-integer second-order cone programming models, for which an effective branch-and-cut algorithm is designed. Numerical experiments on a real case demonstrate the robustness and reliability of our location-routing decisions. The results also illustrate that our data-driven approach outperforms stochastic optimization approach in out-of-sample performance and that the proposed branch-and-cut algorithm surpasses the Gurobi solver in computational efficiency.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111711"},"PeriodicalIF":11.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096663","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":"Reliability sensitivity analysis with multiple failure domains based on an extended two-stage Markov chain Monte Carlo simulation","authors":"Sinan Xiao ,&nbsp;Wolfgang Nowak","doi":"10.1016/j.ress.2025.111718","DOIUrl":"10.1016/j.ress.2025.111718","url":null,"abstract":"<div><div>Understanding how input variables affect the failure of structures is crucial in structural reliability design. The <u>R</u>eliability <u>S</u>ensitivity <u>I</u>ndex (RSI) based on <u>S</u>afe<u>t</u>y/f<u>a</u>ilu<u>r</u>e <u>C</u>lassification <u>o</u>f <u>m</u>odel out<u>p</u>ut (StarComp) quantifies the impact of these uncertain inputs on structural failure. The two-stage Markov Chain Monte Carlo (MCMC) algorithm is efficient for estimating the StarComp RSI, but it only works for problems with a single failure domain. This work extends the two-stage MCMC algorithm to handle problems with multiple disjoint failure domains. In the first stage, initial failure samples in different failure domains are obtained with multiple independent chains. Then, the second stage runs multiple independent Markov chains to sample the failure-conditional PDF. A set of weights is also constructed to obtain a proper estimation of the StarComp RSI. The proposed approach can effectively identify many failure domains with more chains and handle high-dimensional problems with the preconditioned Crank–Nicolson algorithm. It also works for single or overlapping failure domains. Three numerical examples with varying numbers of failure domains and dimensions, and an engineering example of vehicle side impact, are used to test the performance of the proposed approach. The results show that the proposed approach can capture multiple failure domains and obtain correct reliability sensitivity estimates compared to the original approach. It also outperforms subset simulation in computational accuracy and efficiency. With the proposed approach, useful information can be obtained to guide the reliability design of complex structures with multiple failure domains.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"266 ","pages":"Article 111718"},"PeriodicalIF":11.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096671","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}