Journal of Quality Technology最新文献

{"title":"Directional fault classification for correlated High-Dimensional data streams using hidden Markov models","authors":"Yan He, Yicheng Kang, F. Tsung, D. Xiang","doi":"10.1080/00224065.2023.2210320","DOIUrl":"https://doi.org/10.1080/00224065.2023.2210320","url":null,"abstract":"Abstract Modern manufacturing systems are often installed with sensor networks which generate high-dimensional data at high velocity. These data streams offer valuable information about the industrial system’s real-time performance. If a shift occurs in the manufacturing process, fault diagnosis based on the data streams becomes a fundamental task as it identifies the affected data streams and provides insights into the root cause. Existing fault diagnostic methods either ignore the correlation between different streams or fail to determine the shift directions. In this paper, we propose a directional fault classification procedure that incorporates the between-stream correlations. We suggest a three-state hidden Markov model that captures the correlation structure and enables inference about the shift direction. We show that our procedure is optimal in the sense that it minimizes the expected number of false discoveries while controlling the proportion of missed signals at a desired level. We also propose a deconvolution-expectation-maximization (DEM) algorithm for estimating the model parameters and establish the asymptotic optimality for the data-driven version of our procedure. Numerical comparisons with an existing approach and an application to a semiconductor production study show that the proposed procedure works well in practice.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85049001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 non-linear mixed model approach for detecting outlying profiles","authors":"A. V. Quevedo, G. Vining","doi":"10.1080/00224065.2023.2217363","DOIUrl":"https://doi.org/10.1080/00224065.2023.2217363","url":null,"abstract":"Abstract In parametric non-linear profile modeling, it is crucial to map the impact of model parameters to a single metric. According to the profile monitoring literature, using multivariate T statistic to monitor the stability of the parameters simultaneously is a common approach. However, this approach only focuses on the estimated parameters of the non-linear model and treats them as separate but correlated quality characteristics of the process. Consequently, they do not take full advantage of the model structure. To address this limitation, we propose a procedure to monitor profiles based on a non-linear mixed model that considers the proper variance-covariance structure. Our proposed method is based on the concept of externally studentized residuals to test whether a given profile significantly deviates from the other profiles in the non-linear mixed model. The results show that our control chart is effective and appears to perform better than the T chart.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75546634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning Basse R, 2nd edition, Lawrence M. Leemis, 2022, Lightning Source, 368 pp., $40, ISBN: 978-0-9829174-5-9","authors":"Hao Zhao","doi":"10.1080/00224065.2022.2097967","DOIUrl":"https://doi.org/10.1080/00224065.2022.2097967","url":null,"abstract":"R is one of the most common programming languages used for various statistical modeling and data analysis tasks. Learning Base R (2nd edition) by Lawrence M. Leemis provides an accessible approach to R language for beginners with little to no programming exposure. Unlike other introductory R language books, Learning Base R is more in-depth from a statistical perspective, giving a fundamental overview of R language. In this book, Chapter 1 provides a comprehensive introduction to R language, as well as some tricks to make the R session more efficient. The following two chapters introduce basic arithmetic operations. Then, three elementary data structures, vector, matrices, and arrays, are introduced in Chapters 4 to 6. Chapters 7 and 8 describe built-in and user-written functions, and Chapter 9 introduces some useful utilities. Notably, some new functions have been added to these chapters in this new edition, such as assign, append, and attributes. The next three chapters introduce three other types of elements that can be stored in data structures, complex numbers, character strings, and logical elements. Chapters 13 and 14 introduce the methods for comparing elements with relational operators and coercing elements to specific data types. In addition, a new table summarizing the concept of “is family” of functions is provided in this chapter. Two more advanced data structures, lists and data frames, are introduced in the next two chapters. Chapter 17 shows some built-in data sets in R. Chapter 18 concerns input/output; a more sophisticated application of scan is also introduced here. After introducing these essential topics, some advanced topics are illustrated in the following chapters. Chapter 19 focuses on some suitable functions associated with the probability distributions of random variables. Chapters 20 and 21 give, in very fine detail, how to generate high-level graphics and custom graphics, and Chapters 22 to 24 introduce many of R’s programming capabilities. Chapter 25 explains the topic of the Monto Carlo simulation. Furthermore, Chapters 26 to 28 have the most modification comparing to the previous version. Some brief introductions to statistical inference methods are given in Chapter 26, which includes univariate data analysis, analysis of variance, regression, and time series analysis. Chapter 27 introduces linear algebra functions. Chapter 28 covers some popular packages for data visualization and data analysis, such as ggplot2, lubridate, lpsolve, and other packages in the exercises section. There are over 400 exercises in total, an increase of 265 new exercises (an average of 9–10 new exercises per chapter) from the previous edition, to enhance the reader’s knowledge of R. The book also includes plenty of instructional videos and code for readers to explore, which are available on the author’s website. In conclusion, this book covers the R programming language and all its details in a practical way. For those who are just startin","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85824131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 family of orthogonal main effects screening designs for mixed-level factors","authors":"B. Jones, R. Lekivetz, C. Nachtsheim","doi":"10.1080/00224065.2023.2196455","DOIUrl":"https://doi.org/10.1080/00224065.2023.2196455","url":null,"abstract":"Abstract There is limited literature on screening when some factors are at three levels and others are at two levels. This topic has seen renewed interest of late following the introduction of the definitive screening design structure by Jones and Nachtsheim 2011 and Xiao et al. 2012. Two well-known examples are Taguchi’s L 18 and L 36 designs. However, these designs are limited in two ways. First, they only allow for either 18 or 36 runs, which is restrictive. Second, they provide no protection against bias of the main effects due to active two-factor interactions. In this article, we introduce a family of orthogonal, mixed-level screening designs in multiples of eight runs. Our 16-run design can accommodate up to four continuous three-level factors and up to eight two-level factors. The three-level factors must be continuous, whereas the two-level factors can be either continuous or categorical. All of our designs supply substantial bias protection of the main effects estimates due to active two-factor interactions.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79487668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blocking OMARS designs and definitive screening designs","authors":"José Núñez Ares, P. Goos","doi":"10.1080/00224065.2023.2196035","DOIUrl":"https://doi.org/10.1080/00224065.2023.2196035","url":null,"abstract":"Abstract The family of orthogonal minimally aliased response surface or OMARS designs comprises traditional response surface designs, such as central composite designs and Box-Behnken designs, as well as definitive screening designs. Key features of OMARS designs are the facts that they are orthogonal for the main effects and that the main effects are not at all aliased with any two-factor interaction effect or with any quadratic effect. In this article, we present a method to arrange the runs of an OMARS design in blocks of equal size, so that the main effects can be estimated independently from the blocks, and the interaction effects and the quadratic effects are confounded as little as possible with the blocks. We show that our new method for blocking OMARS designs offers much flexibility when it comes to choosing the number of runs, the number of blocks and the block sizes, and that it often outperforms the blocking arrangements of definitive screening designs available in the literature and in commercial software.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82587607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistics for Chemical and Process Engineers: A Modern Approach","authors":"Lenny Rahmawati","doi":"10.1080/00224065.2023.2192883","DOIUrl":"https://doi.org/10.1080/00224065.2023.2192883","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82130638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Reliability of Generating Data","authors":"Willis A. Jensen","doi":"10.1080/00224065.2023.2192884","DOIUrl":"https://doi.org/10.1080/00224065.2023.2192884","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76024237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Foundations of Statistics for Data Scientists: With R and Python","authors":"L. Leemis","doi":"10.1080/00224065.2023.2192882","DOIUrl":"https://doi.org/10.1080/00224065.2023.2192882","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78814503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of the bias-corrected parametric bootstrap in sensitivity testing/analysis to construct confidence bounds with accurate levels of coverage","authors":"E. V. Thomas","doi":"10.1080/00224065.2023.2185558","DOIUrl":"https://doi.org/10.1080/00224065.2023.2185558","url":null,"abstract":"Abstract Sensitivity testing often involves sequential design strategies in small-sample settings that provide binary data which are then used to develop generalized linear models. Model parameters are usually estimated via maximum likelihood methods. Often, confidence bounds relating to model parameters and quantiles are based on the likelihood ratio. In this paper, it is demonstrated how the bias-corrected parametric bootstrap used in conjunction with approximate pivotal quantities can be used to provide an alternative means for constructing bounds when using a location-scale model. In small-sample settings, the coverage of bounds based on the likelihood ratio is often anticonservative due to bias in estimating the scale parameter. In contrast, bounds produced by the bias-corrected parametric bootstrap can provide accurate levels of coverage in such settings when both the sequential strategy and method for parameter estimation effectively adapt (are approximately equivariant) to the location and scale. A series of simulations illustrate this contrasting behavior in a small-sample setting when assuming a normal/probit model in conjunction with a popular sequential design strategy. In addition, it is shown how a high-fidelity assessment of performance can be attained with reduced computational effort by using the nonparametric bootstrap to resample pivotal quantities obtained from a small-scale set of parametric bootstrap simulations.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89887387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interaction effects in pairwise ordering model","authors":"Chun-Yen Wang, Dennis K. J. Lin","doi":"10.1080/00224065.2023.2186288","DOIUrl":"https://doi.org/10.1080/00224065.2023.2186288","url":null,"abstract":"Abstract In an order-of-addition (OofA) experiment, the response is a function of the addition order of components. The key objective of the OofA experiments is to find the optimal order of addition. The most popularly used model for OofA experiments is perhaps the pairwise ordering (PWO) model, which assumes that the response can be fully accounted by the pairwise ordering of components. Recently, the PWO model has been extended by adding the interactions of PWO factors, to account for variations caused by the ordering of sets of three or more components, where the interaction term is defined by the multiplication of two PWO factors. This paper introduces a novel class of conditional PWO effect to study the interaction effect between PWO factors. The advantages of the proposed interaction terms are studied. Based on these conditional effects, a new model is proposed. The optimal order of addition can be straightforwardly obtained via the proposed model.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79357274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}