Reliability Engineering & System Safety最新文献_第2页

Statistical multivariate degradation modeling– A systematic review 统计多元退化模型-系统回顾

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-24 DOI: 10.1016/j.ress.2025.111286

Hui Yi , Weiwei Zhang , Guoliang Wang , Xing Zhang , Qingqing Zhai

{"title":"Statistical multivariate degradation modeling– A systematic review","authors":"Hui Yi , Weiwei Zhang , Guoliang Wang , Xing Zhang , Qingqing Zhai","doi":"10.1016/j.ress.2025.111286","DOIUrl":"10.1016/j.ress.2025.111286","url":null,"abstract":"<div><div>By analyzing the historical health status data and identifying the degradation patterns and trends, degradation modeling helps to predict the system reliability, formulate reliable maintenance plans, and prolong the service life of systems. With the increasing structural complexity and functional diversity of modern engineering systems, it is not enough to determine the overall health status of a system merely relying on a single component or performance characteristic (PC). Meanwhile, considering the coupling of components and the impact of the common operating environment, dependence is present between the degradation processes of different components or PCs in the product. Therefore, the multivariate degradation modeling of dependent PCs has attracted widespread attention in recent years. Focusing on the data-driven approaches and especially statistical models, this paper systematically reviews the existing multivariate degradation modeling methods, including general path models, stochastic process models, copula-based methods and other approaches. This review also discusses factors that may affect the modeling of multivariate degradation processes, such as measurement errors and covariate effects. This paper helps to understand the existing studies in a comprehensive and systematic way, which can also inspire further development and applications in multivariate degradation modeling.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111286"},"PeriodicalIF":9.4,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189906","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

Optimal test termination time in reliability growth management for systems with multiple failure modes 多故障模式系统可靠性增长管理中的最优试验终止时间

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111226

Wenjie Dong , Yingsai Cao , Linhan Ouyang

{"title":"Optimal test termination time in reliability growth management for systems with multiple failure modes","authors":"Wenjie Dong , Yingsai Cao , Linhan Ouyang","doi":"10.1016/j.ress.2025.111226","DOIUrl":"10.1016/j.ress.2025.111226","url":null,"abstract":"<div><div>Reliability growth management is the positive improvement in a reliability metric due to implementation of corrective actions upon system design, operation or maintenance process through a dedicated test-analyze-and-fix (TAAF) procedure. Taking the reliability of a complex system is in fact a multidimensional outcome which is a function of various failure modes into account, the mixed-AMSAA model is constructed in this current research based on the mixed Weibull distribution. The parameters in the mixed-AMSAA model are estimated based on the Weibull probability plot (WPP) in the presence of limited failure data and the goodness-of-fit is tested with the Kolmogorov–Smirnov (K-S) statistic. To determine the optimal termination time of the reliability growth test plan for systems with multiple failure modes, a joint optimization framework under the planing objective of minimizing the total cost by considering the release time in terminating the reliability growth test and the quantity of spare parts inventory for corrective actions is proposed. Numerical solutions to the joint optimization model are theoretically analyzed and two cases from real engineering systems are validated to verify the proposed model. Illustrative results show that the mixed-AMSAA model is capable to capture the growth characteristic of systems with multiple failure modes and is effective in determining the optimal termination time of a reliability growth test program.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111226"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135164","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

Physics-embedding multi-response regressor for time-variant system reliability assessment 时变系统可靠性评估的物理嵌入多响应回归量

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111262

Lu-Kai Song , Fei Tao , Xue-Qin Li , Le-Chang Yang , Yu-Peng Wei , Michael Beer

{"title":"Physics-embedding multi-response regressor for time-variant system reliability assessment","authors":"Lu-Kai Song , Fei Tao , Xue-Qin Li , Le-Chang Yang , Yu-Peng Wei , Michael Beer","doi":"10.1016/j.ress.2025.111262","DOIUrl":"10.1016/j.ress.2025.111262","url":null,"abstract":"<div><div>Efficient time-variant reliability assessment for complex systems is of great interest but challenging as the highly complex multiple output responses under time-variant uncertainties are hard to quantify. The task becomes even more challenging if the interconnected dependencies between multiple failure modes are involved. In this study, an eXtreme physics-embedding multi-response regressor (X-PMR) is presented for time-variant system reliability assessment. Firstly, by transforming time-variant multiple responses to time-invariant extreme values, an eXtreme multi-domain transformation concept is presented, to establish the time-invariant multi-input multi-output (TiMIMO) dataset; moreover, by embedding physics/mathematics knowledge into multi-objective ensemble modeling, a physics-embedding multi-response regressor is proposed, to synchronously construct the surrogate model for highly complex multiple output responses. The validation effectiveness and benefit illustration of the X-PMR method are revealed by introducing three numerical systems (i.e., series system, parallel system and series/parallel hybrid system) and a real application system (i.e., dynamic aeroengine turbine blisk), in comparison with a number of state-of-the-art methods investigated in the literature. The current efforts can provide a novel sight to address the time-variant system reliability assessment problems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111262"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168326","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

The mean number of failed components in discrete time consecutive k-out-of-n: F system and its application to parameter estimation and optimal age-based preventive replacement 离散时间连续k- of-n: F系统中失效部件的平均数目及其在参数估计和基于年龄的最优预防性替换中的应用

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111229

Serkan Eryilmaz , Cihangir Kan

引用次数: 0

Practical semi-supervised learning framework for real-time warning of aerodynamic instabilities: Applications from compressors to gas turbine engines 气动不稳定性实时预警的实用半监督学习框架：从压缩机到燃气涡轮发动机的应用

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111261

Xinglong Zhang , Tianhong Zhang

{"title":"Practical semi-supervised learning framework for real-time warning of aerodynamic instabilities: Applications from compressors to gas turbine engines","authors":"Xinglong Zhang , Tianhong Zhang","doi":"10.1016/j.ress.2025.111261","DOIUrl":"10.1016/j.ress.2025.111261","url":null,"abstract":"<div><div>This study introduces a semi-supervised learning framework for the aerodynamic instability warning in gas turbine engines, emphasizing effectiveness, generalization, and practicality. The initial preprocessing involves low-pass filtering and downsampling to mitigate noise and high-frequency disruptions in the pressure signal at the compressor outlet. A 5 ms sliding time window then segments the pressure data, followed by the adaptive wavelet synchrosqueezed transform (AWSST) for sample labeling. To address significant dataset imbalance, an anomaly detection approach is adopted, incorporating feature selection with ReliefF and mutual information, a sparse autoencoder with bidirectional gated recurrent units (BiGRU-SAE), and a warning logic based on reconstruction errors and pressure drop amplitudes. The framework's effectiveness and generalization are evaluated across all datasets and validated through real-time warning experiments on a hardware-in-the-loop (HIL) simulation platform. Results show that our method detects instabilities 20 to 45 ms earlier than monitoring the pressure change rate, with a single-step computation time of approximately 3 ms, well within the requirements for real-time processing. This improvement in early detection can significantly enhance engine safety and performance. Notably, our method demonstrates generalizability across different states of the same engine and between different engines, suggesting its potential for developing a widely applicable warning model with limited data.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111261"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205364","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

Uncertainty evaluation of the debris flow impact considering spatially varying basal friction and solid concentration 考虑空间变化基础摩擦和固体浓度的泥石流影响的不确定性评价

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111283

Hongyu Luo , Limin Zhang , Jian He , Jiawen Zhou

{"title":"Uncertainty evaluation of the debris flow impact considering spatially varying basal friction and solid concentration","authors":"Hongyu Luo , Limin Zhang , Jian He , Jiawen Zhou","doi":"10.1016/j.ress.2025.111283","DOIUrl":"10.1016/j.ress.2025.111283","url":null,"abstract":"<div><div>The inherent spatial variability of soil is reported to significantly impact landslide debris behaviors. In this study, the effect of spatial variability on the inundation and impact processes of debris flow is investigated using a multi-phase depth-averaged model. The dynamic process of a debris flow, considering spatial variabilities of basal friction and initial solid concentration, is explored via Monte Carlo simulation. The results show that due to the flow channel constrain and spatial averaging, the influences of spatial variability on the global impact of debris flow are not significant. However, remarkable influences on the local impact are found. From the upstream of flow channel to the downstream of river, there is a decreasing trend in uncertainties regarding the material composition and flow dynamics at local spots. In the flow channel, the mean values of flow depths are smaller than those in the deterministic analysis, while those of flow velocities are larger. In the river, both the mean values of flow depths and velocities are close to those in the deterministic analysis while their variations remain significant even downstream of river. The findings provide insights into the spatial variability effects on debris flow impact and facilitate risk assessment.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111283"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147878","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

Estimation of economic loss and recover process after earthquake base on nighttime light data and time series model 基于夜间灯光数据和时间序列模型的地震后经济损失及恢复过程估算

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111244

Jinpeng Zhao , Xiaojun Li , Su Chen

{"title":"Estimation of economic loss and recover process after earthquake base on nighttime light data and time series model","authors":"Jinpeng Zhao , Xiaojun Li , Su Chen","doi":"10.1016/j.ress.2025.111244","DOIUrl":"10.1016/j.ress.2025.111244","url":null,"abstract":"<div><div>This study develops a framework for predicting Nighttime Light (NTL) data trends without earthquakes and estimating economic losses and recovery processes after an earthquake. Utilizing Geographic Information System (GIS) technology, the framework unfolds through five stages: data acquisition and extraction, data transformation and calibration, model training and tuning for each 500 m x 500 m grid in the Jiuzhaigou earthquake area in Sichuan Province, China, and calculation of economic losses by comparing NTL predictions and actual observations. By integrating socio-economic characteristics and Damage Index data with NTL trends, and adjusting for model performance through hyperparameter tuning, this research quantifies economic impacts after an earthquake. Final estimates of economic losses and recovery are derived by allocating losses according to each grid’s calculated weight. This study not only predicts NTL data trends in scenarios without earthquake but also contributes novel methods for evaluating economic losses and recovery from earthquakes, presenting an effective framework for similar disaster impact studies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111244"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167597","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

Enhancing power grid resilience during tropical cyclones: Deep learning-based real-time wind forecast corrections for dynamic risk prediction 增强热带气旋期间电网的恢复能力：基于深度学习的实时风预报校正动态风险预测

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111284

You Wu , Naiyu Wang , Xiubing Huang , Zhenguo Wang

{"title":"Enhancing power grid resilience during tropical cyclones: Deep learning-based real-time wind forecast corrections for dynamic risk prediction","authors":"You Wu , Naiyu Wang , Xiubing Huang , Zhenguo Wang","doi":"10.1016/j.ress.2025.111284","DOIUrl":"10.1016/j.ress.2025.111284","url":null,"abstract":"<div><div>Tropical cyclones (TCs) pose severe risks to power transmission systems, yet conventional Numerical Weather Prediction (NWP) models lack the resolution to resolve sub-kilometer wind dynamics critical for infrastructure risk assessment. This study introduces a Real-time Wind Forecast Correction (RWFC) model, a deep learning framework that dynamically refines mesoscale NWP forecasts during TCs by assimilating multi-source observational data. The RWFC integrates Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-BiLSTM) to capture spatiotemporal wind-terrain interactions, with a custom loss function balancing accuracy and conservative bias for proactive risk mitigation. Validated during Typhoon Hagupit (2020) in Zhejiang Province, China, the RWFC reduced wind speed and direction mean absolute errors (MAE) by 78 % (6.47 to 1.41 m/s) and 50 % (53.57° to 26.79°), respectively, compared to raw NWP forecasts. By interpolating corrections from sparse observational sites, it achieved province-scale MAE reductions of 56 %, demonstrating robust generalizability. When applied to Zhejiang’s transmission grid, RWFC lowered the number of projected high-risk towers by 98 %, enabling precise, terrain-sensitive risk predictions. The framework bridges NWP’s physical rigor with deep learning’s adaptive capacity, offering a scalable solution for enhancing grid resilience during evolving TCs. This work advances real-time disaster management by transforming coarse forecasts into actionable, high-resolution risk insights for critical infrastructure.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111284"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184860","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

Deep auto-encoded Conditional Gaussian Mixture Model for warranty claims forecasting 用于保修索赔预测的深度自编码条件高斯混合模型

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111230

Wael Hassanieh , Abdallah Chehade , Vasiliy Krivtsov

{"title":"Deep auto-encoded Conditional Gaussian Mixture Model for warranty claims forecasting","authors":"Wael Hassanieh , Abdallah Chehade , Vasiliy Krivtsov","doi":"10.1016/j.ress.2025.111230","DOIUrl":"10.1016/j.ress.2025.111230","url":null,"abstract":"<div><div>Forecasting warranty claims enables (i) identifying and rectifying quality and reliability problems at the early stages of production, (ii) improving future product designs, and (iii) efficiently allocating financial resources for warranty and maintenance. Nevertheless, precise forecasting of warranty claims is challenging due to the data maturation phenomenon, which is characterized by challenges that include market demand variability, reporting delays, heterogeneous production quality, and customer late claim rush. This paper proposes the Deep auto-encoded Conditional Gaussian Mixture Model (DCGMM) for warranty claims forecasting. DCGMM is a novel hybrid Bayesian and deep learning framework. The method learns a prior joint distribution between auto-encoded temporal latent embeddings extracted from early observed cumulative warranty claims and long-term mature warranty claims based on historical products. DCGMM then uses Bayesian inference to forecast the mature warranty claims of an in-service product of interest conditioned on its auto-encoded embeddings extracted from its limited early claims observations. DCGMM is robust to product variability and complex temporal trends because of its ability to identify different cluster or sub-population behaviors. Furthermore, learning a lower-dimensional space is essential to achieving tractable Bayesian inferences. We validate the performance of the DCGMM for warranty claims forecasting on a large-scale automotive case study and compare it to other state-of-the-art time-series forecasting algorithms.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111230"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135157","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

Quantitative analysis of risk propagation in urban rail transit: A novel ensemble learning method based on the structure of Bayesian Network 城市轨道交通风险传播定量分析：一种基于贝叶斯网络结构的集成学习方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-22 DOI: 10.1016/j.ress.2025.111280

Yuanxi Xu, Keping Li, Yanyan Liu

{"title":"Quantitative analysis of risk propagation in urban rail transit: A novel ensemble learning method based on the structure of Bayesian Network","authors":"Yuanxi Xu, Keping Li, Yanyan Liu","doi":"10.1016/j.ress.2025.111280","DOIUrl":"10.1016/j.ress.2025.111280","url":null,"abstract":"<div><div>The quantification of risk propagation in urban rail transit systems is a critical task to ensure the safe operations. In this study, a novel ensemble learning method based on Bayesian network structure learning is developed to describe the risk propagation mechanisms. The proposed model addresses the reliance problem on ordering and is capable to quantify more complex risk propagation paths. First, an information-based criterion and a score function are proposed to construct the initial propagation structure. Second, a structure constructing algorithm is introduced to generate multiple Bayesian networks, forming a Bayesian Forest. Finally, three applications of the Bayesian Forest are introduced: scenario inference, sensitivity analysis and risk propagation chain evaluation. Additionally, a case study is made on the application of the proposed model to the Shanghai metro system to verify its effectiveness. The results validate the rationality of the ensemble learning method by analyzing multiple risk propagation paths. The interaction characteristics are explicitly described by sensitivity of risk factors and the significance of the risk propagation chain is accurately evaluated.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111280"},"PeriodicalIF":9.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147879","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