(2021). Advanced Survival Models. Journal of Quality Technology. Ahead of Print.

Abstract An integral part of the design of control charts, including the multivariate exponentially weighted moving average (MEWMA) control chart, is the determination of the appropriate control limits for prospective monitoring. Methods using Markov chain analyses, integral equations, and simulation have been proposed to determine the MEWMA chart limits when the limits are based on a specified in-control

Abstract Quality in the trucking industry involves several facets, including on-time performance and safety. In the largest naturalistic driving study to-date, with 496 drivers and 13 M miles driven, we address two safety questions: (a) does the occurrence of safety critical events increase during a driving shift? and (b) what is the effect of rest breaks on the incidence of those events? To address

(2021). Understanding elections through statistics by Ole J. Forsberg, CRC press, Taylor & Francis group, boca Raton, FL, 2020, 225 pp., $69.95, ISBN 978-0367895372. Journal of Quality Technology. Ahead of Print.

Abstract The production of novel types of complex shapes is nowadays enabled by new manufacturing paradigms such as additive manufacturing, also known as 3D printing. The continuous increase of shape complexity imposes new challenges in terms of inspection, product qualification and process monitoring methodologies. Previously proposed methods for 2.5D free-form surfaces are no longer applicable in

Abstract Modern industrial assets (e.g., generators, turbines, engines) are outfitted with numerous sensors to monitor key operating and environmental variables. Unusual sensor readings, such as high temperature, excessive vibration, or low current, could trigger rule-based actions (also known as faults) that range from warning alarms to immediate shutdown of the asset to prevent potential damage.

Abstract A robust multivariate quality control technique for individual observations is proposed, based on the robust reweighted shrinkage estimators. A simulation study is done to check the performance and compare the method with the classical Hotelling approach, and the robust alternative based on the reweighted minimum covariance determinant estimator. The results show the appropriateness of the

Abstract A computer model can be used for predicting an output only after specifying the values of some unknown physical constants known as calibration parameters. The unknown calibration parameters can be estimated from real data by conducting physical experiments. This paper presents an approach to optimally design such a physical experiment. The problem of optimally designing a physical experiment

Abstract In design of experiments, setting exact replicates of factor settings enables estimation of pure-error; a model-independent estimate of experimental error useful in communicating inherent system noise and testing model lack-of-fit. Often in practice, the factor levels for replicates are precisely measured rather than precisely set, resulting in near-replicates. This can result in inflated

Abstract Performing online monitoring for short horizon data is a challenging, though cost effective benefit. Self-starting methods attempt to address this issue adopting a hybrid scheme that executes calibration and monitoring simultaneously. In this work, we propose a Bayesian alternative that will utilize prior information and possible historical data (via power priors), offering a head-start in

Abstract Due to recent advances in the development of laboratory equipment, large screening experiments can now be conducted to study the joint impact of up to a few dozen factors. While much is known about orthogonal designs involving 64 and 128 runs, there is a lack of literature on screening designs with intermediate run sizes. In this article, we therefore construct screening designs with 80, 96

Abstract Monitoring several correlated quality characteristics of a process is common in modern manufacturing and service industries. Although a lot of attention has been paid to monitoring the multivariate process mean, not many control charts are available for monitoring the covariance matrix. This paper presents a comprehensive overview of the literature on control charts for monitoring the covariance

Abstract In multistage manufacturing systems, modeling multiple quality indices based on the process sensing variables is important. However, the classic modeling technique predicts each quality variable one at a time, which fails to consider the correlation within or between stages. We propose a deep multistage multi-task learning framework to jointly predict all output sensing variables in a unified

Abstract Variational autoencoders have been recently proposed for the problem of process monitoring. While these works show impressive results over classical methods, the proposed monitoring statistics often ignore the inconsistencies in learned lower-dimensional representations and computational limitations in high-dimensional approximations. In this work, we first manifest these issues and then overcome

Abstract We propose a method for monitoring proportions when the in-control proportion and the sample sizes vary over time. Our approach is able to overcome some of the performance issues of other commonly used methods, as we demonstrate in this paper using analytical and numerical methods. The derivations and results are shown mainly for monitoring proportions, but we show how the method can be extended

Abstract Dynamic data detection is one of the main concerns in the statistical process control (SPC) field. Here we focus on monitoring parametric multivariate dynamic data streams using the ARMAX-GARCH model, which reflects both the influence of exogenous variables on the mean vector and the heterogeneity of the covariance matrix. A quasi maximum likelihood estimator is used to estimate the parameter

(2021). Next Editor of the Journal of Quality Technology: Dr. L. Allison Jones-Farmer. Journal of Quality Technology: Vol. 53, No. 2, pp. 217-217.

(2021). Correction. Journal of Quality Technology: Vol. 53, No. 2, pp. 218-218.

(2021). The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach. Journal of Quality Technology. Ahead of Print.

Abstract In this article, I provide a detailed discussion of the well-known fish patty experiment introduced in the literature by the late John A. Cornell in the first edition of his famous textbook on the design and analysis of mixture experiments. Cornell used the fish patty experiment as the motivating example for an article discussing that, for logistical reasons, many mixture-process variable

Abstract Big Data are huge amounts of digital information that rarely result from properly planned surveys; as a consequence they often contain redundant observations. When the aim is to answer particular questions of interest, we suggest selecting a subsample of units that contains the majority of the information to achieve this goal. Selection methods driven by the theory of optimal design incorporate

Abstract A manufacturing system collects big and heterogeneous data for tasks such as product quality modeling and data-driven decision-making. However, as the size of data grows, timely and effective data utilization becomes challenging. We propose an unsupervised data filtering method to reduce manufacturing big data sets with multi-variate continuous variables into informative small data sets. Furthermore

Abstract In experimental designs, it is usually assumed that the data follow normal distributions and the models have linear structures. In practice, experimenters may encounter different types of responses and be uncertain about model structures. If this is the case, traditional methods, such as the ANOVA and regression, are not suitable for data analysis and model selection. We introduce the random

Abstract Thermal fields exist widely in engineering systems and are critical for engineering operation, product quality and system safety in many industries. An accurate prediction of thermal field distribution, that is, acquiring any location of interest in a thermal field at the present and future time, is essential to provide useful information for the surveillance, maintenance, and improvement

(2020). Statistical Inference via Data Science: A Modern Dive into R and the Tidyverse. Journal of Quality Technology. Ahead of Print.

(2020). Correction. Journal of Quality Technology. Ahead of Print.

Abstract Since parameter estimation degrades chart performance, it is important to design a control chart correctly, that is, taking account of the estimation effects. To this end, two perspectives are available in the literature: the unconditional, which focuses on the unconditional in-control (IC) average run length ( ARL 0 ), and the conditional, which focuses on the IC run-length distribution conditioned

Models with ordinal outcomes are an important part of generalized linear models and design issues for them are less studied, especially when the model has discrete and continuous factors. We propos...

Abstract User-generated content including both review texts and user ratings provides important information regarding the customer-perceived quality of online products and services. This article proposes a modeling and monitoring method for online user-generated content. A unified generative model is constructed to combine words and ratings in customer reviews based on their latent sentiment and topic

Abstract Recent advances in data acquisition and storage technologies have permitted the rapid collection of data over time at a relatively low cost. The implication of these advances to modern quality engineering is that many of today’s processes produce samples of individual observations that are grossly non-normal and, potentially, very highly autocorrelated. Consequently, the typical assumptions

Accelerated life testing (ALT) is a commonly used experiment in industries for assessing a product’s lifetime. Planning a proper test with consideration of possible constraints on randomization is ...

In this case study, we demonstrate the use of multilevel process monitoring in quality control. Using high school data, we answer three research questions related to high school student progress du...

(2020). The analytics lifecycle toolkit: A practical guide for an effective analytics capability. Journal of Quality Technology. Ahead of Print.

(2020). ASQ Membership. Journal of Quality Technology: Vol. 52, No. 4, pp. 431-431.

(2020). ASQ Books. Journal of Quality Technology: Vol. 52, No. 4, pp. 432-432.

The high-dimensionality and volume of large-scale streaming data has inhibited significant research progress in developing an integrated monitoring and diagnostics (M&D) approach. Such data streams...

Monitoring multivariate and high-dimensional data streams is often an essential requirement for quality management in manufacturing and service sectors in the Industry 4.0 era. Identifying a suitable distribution for a multivariate data set, especially when the number of variables is much larger than the sample size, is often challenging. Consequently, in a high-dimensional set-up, that is, when the

A new data fusion and network analytics framework is proposed that is based on the topology of large-scale networks and the stochastic dependencies between nodes, edges, and sensor data. The framework can transform real-time sensor data collected from disparate sources in a network to detect the location of anomalies and the nodes that are impacted by the detected anomalies. By intelligently fuzing

Directed graphical models aim to represent the probabilistic relationships between variables in a system. Learning a directed graphical model from data includes parameter learning and structure learning. Several methods have been developed for directed graphical models with scalar variables. However, the case in which the variables are infinite-dimensional has not been studied thoroughly. Nowadays

In functional sequential process monitoring, a process is characterized by sequences of observations called profiles which are monitored over time for stability. The goal is to halt a process when the process generating these observations deviates from a specified in control standard. We propose a Bayesian sequential process control (SPC) methodology which uses wavelets to monitor the functional responses

(2020). In memoriam: Raymond “Ray” Harold Myers (1937 – 2020) Journal of Quality Technology: Vol. 52, No. 3, pp. 327-327.

(2020). Mathematical Statistics. Journal of Quality Technology. Ahead of Print.

In applications, most processes for quality control and management are multivariate. Thus, multivariate statistical process control (MSPC) is an important research problem and has been discussed ex...

Computer models with both quantitative and qualitative inputs frequently arise in science, engineering and business. Mixed-input Gaussian process models have been used for emulating such models. Th...

(2021). Probability, Lawrence M. Leemis. Lighting Source, La Vergne, TN, 2017, 566 pp., $45.00, ISBN: 978-0982917473. Journal of Quality Technology: Vol. 53, No. 3, pp. 332-332.

To online monitor the longitudinal performance of processes and give early signals to processes with irregular patterns, a series of dynamic screening systems (DySS) have been proposed in the liter...

The purpose of this article is to persuade experimenters to choose A-optimal designs rather than D-optimal designs for screening experiments. The primary reason for this advice is that the A-optima...

This book offers a good coverage of Gaussian Process for computer simulation experiments. Different from the classic textbook on this topic Santner et al. (2018), as the author stated, “this book m...

This paper investigates degradation modeling under dynamic conditions and its applications. Both univariate and multiple competing degradation processes are considered with individual degradation p...

In statistical process control (SPC), a proper Phase I analysis is essential to the success of Phase II monitoring. With recent advances in sensing technology and data acquisition systems, Phase I ...

(2020). The Road to Quality Control: the Industrial Application of Statistical Quality Control. Journal of Quality Technology. Ahead of Print.

Abstract After completing the experimental runs of a screening design, the responses under study are analyzed by statistical methods to detect the active effects. To increase the chances of correctly identifying these effects, a good analysis method should provide alternative interpretations of the data, reveal the aliasing present in the design, and search only meaningful sets of effects as defined

Abstract This study proposes an efficient approach to detect one or more change points for gamma distribution. We plug a closed-form estimator into the gamma log-likelihood function to obtain a sharp approximation to the maximum of log-likelihood. We further derive a closed form calibration of approximate likelihood which is asymptotically equivalent to the exact log-likelihood. This circumvents iterative

Abstract Space-filling designs are a convenient and effective approach for exploring the input space for experiments. However, standard choices for these designs strive to provide uniform density of points throughout the region of interest. There are numerous situations where flexibility to adapt the density of points to match specific design objectives would be advantageous to maximize the efficiency

Abstract Effective process monitoring of high-dimensional data streams with embedded spatial structures has been an arising challenge for environments with limited resources. Utilizing the spatial structure is key to improve monitoring performance. This article proposes a correlation-based dynamic sampling technique for change detection. Our method borrows the idea of Upper Confidence Bound algorithm

Abstract Stochastic textured surface (STS) data (e.g., material microstructure microscopy images) are increasingly common in many quality control settings. Because of their stochastic nature, performing statistical process control (SPC) for STS data without requiring advanced knowledge of abnormal behavior is challenging, and there is no existing SPC software available to solve this problem. This article

Fully-sequential (i.e., with design points added one-at-a-time) space-filling designs are useful for global surrogate modeling of expensive computer experiments when the number of design points req...

This book is a timely addition to the vast literature on design of experiments. Although there have been many design books, most of them focus on linear models and some of them may devote one or tw...

In classical response surface methodology, the optimization step uses a small number of important factors. However, in practice, experimenters sometimes fit a second-order model without previous ex...

(2021). Time series: a data analysis approach using R. Journal of Quality Technology: Vol. 53, No. 3, pp. 331-331.