Quality and Reliability Engineering International最新文献_第2页

A new multivariate gamma process model for degradation analysis 用于降解分析的新型多元伽马过程模型

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-28 DOI: 10.1002/qre.3646

Kai Song

{"title":"A new multivariate gamma process model for degradation analysis","authors":"Kai Song","doi":"10.1002/qre.3646","DOIUrl":"https://doi.org/10.1002/qre.3646","url":null,"abstract":"In reliability engineering, it is frequently encountered that multiple performance characteristics (PCs) deteriorate simultaneously. The associated degradation processes are usually dependent and exhibit some heterogeneity from unit to unit, which makes the multivariate degradation modeling and reliability evaluation more challenging. To this end, we propose a new multivariate gamma process model. This model introduces a multivariate random vector, whose joint distribution is constructed by marginal gamma distributions and a copula function, to describe the unit‐to‐unit variability and the dependence among PCs. Meanwhile, it does not require all PCs to be inspected at the same time points in contrast to the traditional copula‐based degradation models. In addition, two reliability evaluation methods are developed. Model parameters are estimated by the stochastic expectation maximization algorithm, and a three‐step procedure is provided to initialize this algorithm. Subsequently, numerical simulations are implemented to verify the proposed methods. Finally, two examples are provided for illustration, and it is shown that the proposed model and methods scale well to the degradation data with different numbers of PCs. What is more, comparisons with several benchmark models are performed, and the superiority of the proposed model is well demonstrated.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine 将改进的金枪鱼群优化技术与图卷积神经网络相结合，计算发动机的剩余使用寿命

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-28 DOI: 10.1002/qre.3651

Yongliang Yuan, Qingkang Yang, Guohu Wang, Jianji Ren, Zhenxi Wang, Feng Qiu, Kunpeng Li, Haiqing Liu

{"title":"Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine","authors":"Yongliang Yuan, Qingkang Yang, Guohu Wang, Jianji Ren, Zhenxi Wang, Feng Qiu, Kunpeng Li, Haiqing Liu","doi":"10.1002/qre.3651","DOIUrl":"https://doi.org/10.1002/qre.3651","url":null,"abstract":"Accurate prediction of the engine's remaining useful life (RUL) is essential to ensure the safe operation of the aircraft because. However, traditional deep‐learning based methods for RUL prediction has been limited by interpretability and adjustment for hyperparameters in practical applications due to the intricate potential relations during the degradation process. To address these dilemmas, an improved multi‐strategy tuna swarm optimization‐assisted graph convolutional neural network (IMTSO‐GCN) is developed to achieve RUL prediction in this work. Specifically, mutual information is used to describe potential relationships among measured parameters so that they could be utilized in building edges for these parameters. Besides, considering that not all relational nodes will positively affect the RUL prediction and the inherent hyperparameters of the GCN are high‐dimensional. Inspired by “No Free Lunch (NFL)”, IMTSO is proposed to reduce the optimization cost of hyperparameters, in which cycle chaotic mapping is employed to achieve initialization of the population for improving the uniformity of the initial population distribution. Besides, a novel adaptive approach is proposed to enhance the exploration and exploitation of tuna swarm optimization (TSO). The CMAPSS dataset was used to validate the effectiveness and advancedness of IMTSO‐GCN, and the experimental results show that the R2 of the IMTSO‐GCN is greater than 0.99, RMSE is less than 3, the Score error is within 1.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Sensitivity and reliability comparisons of EWMA mean control chart based on robust scale estimators under non‐normal process: COVID data application 非正态过程下基于稳健标度估计器的 EWMA 均值控制图的灵敏度和可靠性比较：COVID 数据应用

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-28 DOI: 10.1002/qre.3649

Nadia Saeed, Ala'a Mahmoud Falih Bataineh, Moustafa Omar Ahmed Abu‐Shawiesh, Firas Haddad

{"title":"Sensitivity and reliability comparisons of EWMA mean control chart based on robust scale estimators under non‐normal process: COVID data application","authors":"Nadia Saeed, Ala'a Mahmoud Falih Bataineh, Moustafa Omar Ahmed Abu‐Shawiesh, Firas Haddad","doi":"10.1002/qre.3649","DOIUrl":"https://doi.org/10.1002/qre.3649","url":null,"abstract":"Robust control charts are getting vital importance in statistical process control theory as they are insensitive to the departure from normality. Therefore, the main objective of current work is to determine the effects of non‐normal process on the Exponentially Weighted Moving Average (EWMA) control chart. To achieve this goal, the sensitivity and reliability comparisons are made under the non‐normal process by comparing five robust M‐scale estimators, suggested in literature to modify the EWMA control limits for monitoring process mean and on the basis of percentile bootstrap estimator. The paper addresses the run length (RL) distribution of a robust EWMA control chart under the non‐normal process for which the exponential distribution is used as non‐normal process. The standard deviation of RL, out‐of‐control average run length (ARL), and shift detection probabilities are examined to assess the sensitivity and reliability of robust EWMA control charts for mean of monitoring process. The results of this research indicate that the classical EWMA control chart's performance is substantially impacted by the non‐normal distribution and the proposed EWMA control charts show higher sensitivity than classical one in terms of having smaller values of out‐of‐control ARLs. A real‐life example from the medical sciences field is provided the practical usage of the proposed control charts. The simulation analysis and practical example have shown that the suggested control charts are effective in quickly monitoring the out‐of‐control process.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Weibull mixture estimation based on censored data with applications to clustering in reliability engineering 基于有删减数据的 Weibull 混合估计，在可靠性工程中的聚类应用

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-27 DOI: 10.1002/qre.3647

Florian Lamalle, Vincent Feuillard, Anne Sabourin, Stephan Clémençon

引用次数: 0

Textual data for electricity load forecasting 用于电力负荷预测的文本数据

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-24 DOI: 10.1002/qre.3637

David Obst, Sandra Claudel, Jairo Cugliari, Badih Ghattas, Yannig Goude, Georges Oppenheim

{"title":"Textual data for electricity load forecasting","authors":"David Obst, Sandra Claudel, Jairo Cugliari, Badih Ghattas, Yannig Goude, Georges Oppenheim","doi":"10.1002/qre.3637","DOIUrl":"https://doi.org/10.1002/qre.3637","url":null,"abstract":"Traditional mid‐term electricity forecasting models rely on calendar and meteorological information such as temperature and wind speed to achieve high performance. However depending on such variables has drawbacks, as they may not be informative enough during extreme weather. While ubiquitous, textual sources of information are hardly included in prediction algorithms for time series, despite the relevant information they may contain. In this work, we propose to leverage openly accessible weather reports for electricity demand and meteorological time series prediction problems. Our experiments on French and British load data show that the considered textual sources allow to improve overall accuracy of the reference model, particularly during extreme weather events such as storms or abnormal temperatures. Additionally, we apply our approach to the problem of imputation of missing values in meteorological time series, and we show that our text‐based approach beats standard methods. Furthermore, the influence of words on the time series' predictions can be interpreted for the considered encoding schemes of the text, leading to a greater confidence in our results.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Inference of multi‐sample stage life testing model under Weibull distribution Weibull 分布下多样本阶段寿命测试模型的推断

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-22 DOI: 10.1002/qre.3642

Debashis Samanta, Debasis Kundu

引用次数: 0

Stochastic modeling and optimization of turbogenerator performance using meta‐heuristic techniques 利用元启发式技术对涡轮发电机性能进行随机建模和优化

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-22 DOI: 10.1002/qre.3639

Deepak Sinwar, Naveen Kumar, Ashish Kumar, Monika Saini

{"title":"Stochastic modeling and optimization of turbogenerator performance using meta‐heuristic techniques","authors":"Deepak Sinwar, Naveen Kumar, Ashish Kumar, Monika Saini","doi":"10.1002/qre.3639","DOIUrl":"https://doi.org/10.1002/qre.3639","url":null,"abstract":"The objective of this paper is to identify the most sensitive component of a turbogenerator and optimize its availability. To achieve this, we begin by conducting an initial reliability, availability, maintainability, and dependability (RAMD) analysis on each component. Subsequently, a novel stochastic model is developed to analyze the steady‐state availability of the turbogenerator, employing a Markov birth‐death process. In this model, failure and repair rates are assumed to follow an exponential distribution and are statistically independent. To optimize the proposed stochastic model, we employ four population‐based meta‐heuristic approaches: the grey wolf optimization (GWO), the dragonfly algorithm (DA), the grasshopper optimization algorithm (GOA), and the whale optimization algorithm (WOA). These algorithms are utilized to find the optimal solution by iteratively improving the availability of the turbogenerator. The performance of each algorithm is evaluated in terms of system availability and execution time, allowing us to identify the most efficient algorithm for this task. Based on the numerical results, it is evident that the WOA outperforms the GWO, GOA, and DA in terms of both system availability and execution time.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 learning the Hurst parameter of linear fractional processes and assessing its reliability 深度学习线性分数过程的赫斯特参数并评估其可靠性

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-22 DOI: 10.1002/qre.3641

Dániel Boros, Bálint Csanády, Iván Ivkovic, Lóránt Nagy, András Lukács, László Márkus

{"title":"Deep learning the Hurst parameter of linear fractional processes and assessing its reliability","authors":"Dániel Boros, Bálint Csanády, Iván Ivkovic, Lóránt Nagy, András Lukács, László Márkus","doi":"10.1002/qre.3641","DOIUrl":"https://doi.org/10.1002/qre.3641","url":null,"abstract":"This research explores the reliability of deep learning, specifically Long Short‐Term Memory (LSTM) networks, for estimating the Hurst parameter in fractional stochastic processes. The study focuses on three types of processes: fractional Brownian motion (fBm), fractional Ornstein–Uhlenbeck (fOU) process, and linear fractional stable motions (lfsm). The work involves a fast generation of extensive datasets for fBm and fOU to train the LSTM network on a large volume of data in a feasible time. The study analyses the accuracy of the LSTM network's Hurst parameter estimation regarding various performance measures like root mean squared error (RMSE), mean absolute error (MAE), mean relative error (MRE), and quantiles of the absolute and relative errors. It finds that LSTM outperforms the traditional statistical methods in the case of fBm and fOU processes; however, it has limited accuracy on lfsm processes. The research also delves into the implications of training length and valuation sequence length on the LSTM's performance. The methodology is applied to estimating the Hurst parameter in li‐ion battery degradation data and obtaining confidence bounds for the estimation. The study concludes that while deep learning methods show promise in parameter estimation of fractional processes, their effectiveness is contingent on the process type and the quality of training data.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Sensitivity analysis for sets: Application to pollutant concentration maps 集合的敏感性分析：污染物浓度地图的应用

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-22 DOI: 10.1002/qre.3638

Noé Fellmann, M. Pasquier, C. Blanchet‐Scalliet, C. Helbert, A. Spagnol, D. Sinoquet

引用次数: 0

Reliability analysis for manufacturing system of drive shaft based on dynamic Bayesian network 基于动态贝叶斯网络的传动轴制造系统可靠性分析

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-08-22 DOI: 10.1002/qre.3644

Taotao Cheng, Diqing Fan, Xintian Liu, JinGang Wang

引用次数: 0