Quality and Reliability Engineering International最新文献_第4页

A novel reliability analysis approach for multi‐component systems with stochastic dependency and functional relationships 具有随机依赖性和功能关系的多组件系统的新型可靠性分析方法

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-27 DOI: 10.1002/qre.3621

Karim Atashgar, Majid Abbasi, Mostafa Khazaee, Mehdi Karbasian

{"title":"A novel reliability analysis approach for multi‐component systems with stochastic dependency and functional relationships","authors":"Karim Atashgar, Majid Abbasi, Mostafa Khazaee, Mehdi Karbasian","doi":"10.1002/qre.3621","DOIUrl":"https://doi.org/10.1002/qre.3621","url":null,"abstract":"Reliability prediction for complex systems utilizing prognostic methods has attracted increasing attention. Furthermore, achieving accurate reliability predictions for complex systems necessitates considering the interaction between components and the multivariate functional relationship that exists among them. This paper proposes a bi‐level method to evaluate the variability of degradation processes and predictive reliability based on the profile monitoring approach for multicomponent systems. Firstly, a multivariate profile structure is introduced to model the framework of degradation analysis in scenarios where there exists stochastic dependency and a multivariate functional relationship between the degradation processes of components. At the component level, the objective is to evaluate the variability of the degradation process for each component considering the presence of stochastic dependence. For the system level analysis, the proposed approach enables the prediction of degradation variability and system reliability by considering the functional relationships among components, without the need for direct calculation of individual component reliabilities. The performance of the proposed model is evaluated through a numerical study and sensitivity analysis conducted on a multicomponent system with a k‐out‐of‐n structure. The results demonstrate the model's notable flexibility and efficiency.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771801","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

Multimode high‐dimensional time series clustering and monitoring for wind turbine SCADA data 风力涡轮机 SCADA 数据的多模式高维时间序列聚类和监测

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-26 DOI: 10.1002/qre.3626

Luo Yang, Kaibo Wang, Jie Zhou

{"title":"Multimode high‐dimensional time series clustering and monitoring for wind turbine SCADA data","authors":"Luo Yang, Kaibo Wang, Jie Zhou","doi":"10.1002/qre.3626","DOIUrl":"https://doi.org/10.1002/qre.3626","url":null,"abstract":"The operating process of complex systems usually manifest in multiple distinct operating modes. In the case of a wind turbine, for example, its operating mode is highly influenced by the wind condition, which changes dynamically in natural environment. The SCADA system plays a crucial role in collecting various parameters from wind turbines, facilitating the differentiation, and modeling of distinct operating modes. However, the challenge lies in the excessive dimensionality of variables in SCADA data, making modeling efforts both intricate and inefficient. In this study, we leverage the engineering knowledge on the hierarchical structure of the variables in wind turbine, and propose a novel method to efficiently cluster the data temporally by operating modes. Our methodology involves initially clustering variables according to subsystems and implementing temporal clustering within each subsystem. Subsequently, we introduce a novel graph neural network to extract and concatenate features from all subsystems, enabling the discrimination of the operational mode of the entire system. Finally, we model these features to make predictions of the output power, and the prediction residual can be used for monitoring. Performance evaluations on both numerical experiments and real‐world wind turbine datasets attest to the effectiveness and superiority of the proposed methods.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771802","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

Software reliability prediction: A machine learning and approximation Bayesian inference approach 软件可靠性预测：机器学习和近似贝叶斯推理方法

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-26 DOI: 10.1002/qre.3616

Shahrzad Oveisi, Ali Moeini, Sayeh Mirzaei, Mohammad Ali Farsi

{"title":"Software reliability prediction: A machine learning and approximation Bayesian inference approach","authors":"Shahrzad Oveisi, Ali Moeini, Sayeh Mirzaei, Mohammad Ali Farsi","doi":"10.1002/qre.3616","DOIUrl":"https://doi.org/10.1002/qre.3616","url":null,"abstract":"Reliability growth models are commonly categorized into two primary groups: parametric and non‐parametric models. Parametric models, known as Software Reliability Growth Models (SRGM) rely on a set of hypotheses that can potentially affect the accuracy of model predictions, while non‐parametric models (such as neural networks) can predict the model solely based on training data without any assumptions regarding the model itself. In this paper, we propose several methods to enhance prediction accuracy in software reliability context. More specifically, we, on one hand, introduce two gradient‐based techniques for estimating parameters of classical SRGMs. On the other, we propose methods involving LSTM Encoder–Decoder and Bayesian approximation within Langevin Gradient and Variational inference neural networks. To evaluate our proposed models' performance, we compare them with various neural network‐based software reliability models using three real‐world software failure datasets and utilizing the Mean Square Error (MSE) as a model comparison criterion. The experimental results indicate that our proposed non‐parametric models outperform most classical parametric and non‐parametric models.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771803","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

A new kid on the block: The stratification pattern for space‐filling, with dimension by weight tables 新来的孩子空间填充的分层模式，按权重表划分维度

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-25 DOI: 10.1002/qre.3627

Ulrike Grömping

{"title":"A new kid on the block: The stratification pattern for space‐filling, with dimension by weight tables","authors":"Ulrike Grömping","doi":"10.1002/qre.3627","DOIUrl":"https://doi.org/10.1002/qre.3627","url":null,"abstract":"Designs for computer experiments in quantitative factors use columns with many levels. Filling the experimental space is their most important property, and there are many criteria that assess aspects of space‐filling. Recently, Tian and Xu proposed a stratification pattern for assessing the stratification‐related space‐filling properties of designs for quantitative experimental variables whose number of levels is a power of a – usually small – integer. Such designs have been named GSOAs, in generalization of the earlier proposal of strong – or stratum – orthogonal arrays (SOAs). Latin hypercube designs (LHDs) with a suitable number of levels are special cases of GSOAs. Tian and Xu proposed to use the stratification pattern as a means to ranking (G)SOAs. They reported a small simulation study in which arrays that fared well in that ranking performed well in predicting an unknown function. Shi and Xu refined the criterion and also demonstrated success of a design that fares well on their refined criterion. This paper explains the ideas behind the stratification pattern and the related ranking criteria. A practical example and several toy examples aid the illustration. The stratification pattern can be calculated using the R package SOAs, which does not only provide the pattern itself but also provides more detail in a dimension by weight table, in the spirit of the refinement by Shi and Xu.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771804","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

The combined Shewhart–EWMA sign charts 合并的 Shewhart-EWMA 标志图

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-21 DOI: 10.1002/qre.3625

Mohammed Kadhim Shanshool, Shashibhushan B. Mahadik, Dadasaheb G. Godase, Michael B. C. Khoo

引用次数: 0

A novel equipment remaining useful life prediction approach considering dynamic maintenance threshold 考虑动态维护阈值的新型设备剩余使用寿命预测方法

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-17 DOI: 10.1002/qre.3623

Li'Na Ren, Kangning Li, Xueliang Li, Ziqian Wang

{"title":"A novel equipment remaining useful life prediction approach considering dynamic maintenance threshold","authors":"Li'Na Ren, Kangning Li, Xueliang Li, Ziqian Wang","doi":"10.1002/qre.3623","DOIUrl":"https://doi.org/10.1002/qre.3623","url":null,"abstract":"In conventional remaining useful life (RUL) prediction approaches grounded on maintenance, the maintenance threshold is typically established as a stationary value. However, the actual maintenance threshold may exceed its preset value due to the uncertainty of degradation and other factors. Therefore, it is necessary to consider the dynamic maintenance threshold to improve the precision of remaining useful life prediction. By considering the Wiener process, the maintenance threshold error is introduced to reflect the dynamic nature of the maintenance threshold. The influence of maintenance on degradation amount, degradation rate, and degradation path are comprehensively considered to establish a multi‐stage maintenance‐affected degradation process model. The RUL formula of the equipment is derived using the first hitting time (FHT). The maximum likelihood estimation (MLE) approach and Bayesian theory are employed to estimate the model's parameters. The proposed approach is validated using simulation data and gyroscope degradation data. The outcomes reveal that the proposed approach can significantly enhance the precision of life prediction for the equipment.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737979","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

Extended GWMA control charts: A critical evaluation 扩展的 GWMA 控制图：关键评估

IF 2.2 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-15 DOI: 10.1002/qre.3624

Abdul Haq, W. Woodall

引用次数: 0

A functional data‐driven method for modeling degradation of waxy lubrication layer 蜡质润滑层降解建模的功能数据驱动法

IF 2.2 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-15 DOI: 10.1002/qre.3622

Wenda Kang, Shixiang Li, Yubin Tian, Ying Yin, Heliang Sui, Dianpeng Wang

引用次数: 0

Modelling and inference for a degradation process with partial maintenance effects 具有部分维护效果的退化过程的建模和推论

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-13 DOI: 10.1002/qre.3618

Margaux Leroy, Christophe Bérenguer, Laurent Doyen, Olivier Gaudoin

{"title":"Modelling and inference for a degradation process with partial maintenance effects","authors":"Margaux Leroy, Christophe Bérenguer, Laurent Doyen, Olivier Gaudoin","doi":"10.1002/qre.3618","DOIUrl":"https://doi.org/10.1002/qre.3618","url":null,"abstract":"This paper proposes a new way of modelling imperfect maintenance in degradation models, by assuming that maintenance affects only a part of the degradation process. More precisely, the global degradation process is the sum of two dependent Wiener processes with drift. Maintenance has an effect of the ‐type on only one of these processes: it reduces the degradation level of a quantity which is proportional to the amount of degradation of this process accumulated since previous maintenance. Two particular cases of the model are considered: perturbed and partial replacement models. The usual model is also a specific case of this new model. The system is regularly inspected in order to measure the global degradation level. Two observation schemes are considered. In the complete scheme, the degradation levels are measured both between maintenance actions and at maintenance times (just before and just after). In the general scheme, the degradation levels are measured only between maintenance actions. The maximum likelihood estimation of the model parameters is studied for both observation schemes in both particular models. The quality of the estimators is assessed through a simulation study.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611026","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

Editorial for the special issue on experimental design for reliability and life testing 可靠性和寿命测试实验设计》特刊编辑

IF 2.3 3区工程技术

Quality and Reliability Engineering International Pub Date : 2024-07-08 DOI: 10.1002/qre.3620

Rong Pan

引用次数: 0