Abstract 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 during an academic year. The questions are (1) What determines student performance? (2) How can statistical process monitoring be used in monitoring student progress? (3) What method can be used for

(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 are becoming common in various applications including manufacturing, healthcare, and web mining. In this article, we propose an integrated M&D approach for large-scale streaming data. Using principal component

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.

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 extensively in the literature. Early MSPC research is based on the assumptions that process observations at different time points are independent and they have a parametric distribution (e.g., Gaussian) when

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. The key in building this emulator is to accurately estimate the covariance between different categorical levels of the qualitative inputs. This problem is challenging when the number of categorical levels is

(2020). Probability, Lawrence M. Leemis. Lighting Source, La Vergne, TN, 2017, 566 pp., $45.00, ISBN: 978-0982917473. Journal of Quality Technology. Ahead of Print.

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 literature. Existing DySS methods are all based on estimation of the in-control (IC) mean and variance of processes with a regular longitudinal pattern. In this paper, a new DySS method is suggested, which is

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

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-optimality criterion is more consistent with the screening objective than the D-optimality criterion. The goal of screening experiments is to identify an active subset of the factors. An A-optimal design minimizes

(2020). Surrogates: Gaussian process modeling, design, and optimization for the applied sciences. Journal of Quality Technology. Ahead of Print.

This paper investigates degradation modeling under dynamic conditions and its applications. Both univariate and multiple competing degradation processes are considered with individual degradation paths being described by Wiener processes. Parametric and non-parametric approaches are used to capture the effect of environmental conditions on process parameters. For competing degradation processes, we

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 analysis of high-dimensional data is increasingly encountered. However, the high dimensionality presents a new challenge to the traditional Phase I techniques. A literature review reveals nearly no Phase

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

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 by user-specified

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 optimization

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 of the

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 and uses

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 introduces

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 required to achieve a suitable accuracy is unknown in advance. We develop and investigate three fully-sequential space-filling (FSSF) design algorithms that are conceptually simple and computationally efficient

(2020). Design of Experiments for Generalized Linear Models. Journal of Quality Technology. Ahead of Print.

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 experimentation. In this case, the true model is uncertain and the full model may overfit. Here, we use an extensive simulation to evaluate several analysis strategies in terms of their optimum locating ability

(2020). Time series: a data analysis approach using R. Journal of Quality Technology. Ahead of Print.

Multivariate categorical quality characteristics, whose distribution can be displayed by a contingency table, are routinely encountered in many applications. When most of the cell entries in the contingency table are very small or zeros counts, which is so-called sparse contingency table in the literature, existing methods developed in the literature are often inadequate for use, due to the inaccuracy

Nonparametric control charts that can detect arbitrary distributional changes are highly desirable due to their flexibility to adapt to different distributional assumptions and changes. However, most of the nonparametric control charts in the literature either can only detect location changes, or involve intensive computation. In this article, we propose a new nonparametric adaptive CUSUM chart. The

In many applications of modern quality control, process monitoring involves a large number of process variables and quality characteristics. Practitioners are desired to attain complete information about the process in order to assure quick detection of shifts that may possibly occur at any variable. However, full information is not always available during online monitoring of big data streams due

(2019). ASQ Books. Journal of Quality Technology: Vol. 51, No. 4, pp. (422)-(422).

(2019). ASQ Membership. Journal of Quality Technology: Vol. 51, No. 4, pp. (423)-(423).

(2020). Probability and Statistics for Data Sciences: Math + R + Data. Journal of Quality Technology: Vol. 52, No. 4, pp. 428-430.

(2019). Mixture Modeling for Medical and Health Sciences. Journal of Quality Technology. Ahead of Print.

(2020). Analysis of Integrated Data. Journal of Quality Technology: Vol. 52, No. 4, pp. 427-427.

Split-plot definitive screening and blocked definitive screening designs have been developed for detecting active main effects and second-order effects in screening experiments when split-plot and block structures exist. In the literature, multistage regression and forward stepwise regression methods were proposed for data analysis on the two types of designs. However, classical regression approaches

The goal of this case study is to answer four research questions related to fatigue through features derived from wearable sensors to measure patterns in steps: (1) How do important gait parameters change over time? (2) How do these sensor-based changes relate to the participant's subjective fatigue ratings over time? (3) Are there consistent patterns in performance across different individuals over

Abstract Parameter estimation has a large impact on control chart performance. Recently, widened control limits have been suggested to guarantee an acceptable in-control behavior. However, the consequence is a reduced ability to detect a real change in the process. In order to overcome this undesired effect, we explore an alternative design based on a delayed updating of parameter estimates. We consider

Many problems in pattern/image recognition, fault diagnosis, signal processing, anomaly detection, and machine learning generate massive amounts of multidimensional data with multiple aspects and high dimensionality. Multi-way arrays or tensors provide a natural and compact representation for such massive multidimensional data. Big data analytics require new technologies to efficiently deal with huge

Abstract We review the long-established rational subgrouping principle for determining an effective sampling plan for process monitoring. We present some other general advice that has been given in the literature and discuss some issues related to sampling as it applies to monitoring. Because it is very common to form samples by aggregating data over fixed time intervals, we review the literature on

(2019). A review of some sampling and aggregation strategies for basic statistical process monitoring: Discussion by Ross Sparks. Journal of Quality Technology. Ahead of Print.

Invited discussion paper for "A Review of Some Sampling and Aggregation Strategies for Basic Statistical Process Monitoring" by Zwetsloot and Woodall.

We appreciate the opportunity to comment on the article by Zwetsloot and Woodall (2019 Zwetsloot, I. M. and W. H. Woodall. 2019. A review of some sampling and aggregation strategies for basic statistical process monitoring. Journal of Quality Technology. doi: 10.1080/00224065.2019.1611354.[Taylor & Francis Online] , [Google Scholar]). As practitioners, we have seen very little guidance in the literature

The article by Zwetsloot and Woodall (2019 Zwetsloot, I. M. and W. H. Woodall. 2019. A review of some sampling and aggregation strategies for basic statistical process monitoring. Journal of Quality Technology. doi: 10.1080/00224065.2019.1611354.[Taylor & Francis Online] , [Google Scholar]) opportunely addresses an updated and relevant topic that has been escaping the attention of the data-centric/evidenced

(2019). Discussion: A review of some sampling and aggregation strategies for basic statistical process monitoring (I. M. Zwetsloot and W. H. Woodall). Journal of Quality Technology. Ahead of Print.

(2019). Discussion of article by Zwetsloot and Woodall: A review of some sampling and aggregation strategies for basic statistical process monitoring. Journal of Quality Technology. Ahead of Print.

(2019). Rejoinder. Journal of Quality Technology. Ahead of Print.

Abstract Effective oxidation-based elimination of emerging contaminants (ECs) requires a good understanding of the effects of treatment conditions, such as the kinds and dosages of reagents, on the EC degradation rate. Due to limited knowledge on the complex reaction mechanism and the multiple covariates to represent the treatment conditions, it is generally hard to parametrically quantify the relation

Abstract Recently, dual-phase high-strength steel has attracted increasing attention in the automotive industry due to its prominent physical and mechanical properties. Microstructures of dual-phase high-strength steel have a significant effect on the properties of steel, such as wear resistance and strength, so they have an important role in the quality of steel. Therefore, statistical modeling of

(2020). Experimental Design. Journal of Quality Technology: Vol. 52, No. 4, pp. 423-424.

Row–column designs involve two crossed blocking factors. We propose three combinatorial criteria and a criterion based on D-efficiency to identify optimal row–column arrangements of fractional factorial two-level designs. We search for such arrangements in complete catalogs of 16-run and 24-run orthogonal arrays of strength 2 with two-level treatment factors and two multi-level blocking factors. One

Abstract Computer experiments may involve not only continuous input factors but also nominal factors, discrete numeric factors, and ordinal factors. Most existing literature in designing computer experiments focuses only on continuous factors. Some works have further considered nominal factors, but the cases that also contain discrete numeric or ordinal factors are almost overlooked. In this work,

Abstract We apply integrated parameter and tolerance design (IPTD) to optimize the nominal values and tolerances of 10 geometric design variables of a centrifugal compressor based on a computational fluid dynamics simulator. The compressor quality is quantified by the conformance probabilities of three performance measures (PMs) and optimizing the simulator directly is time-consuming. Gaussian process

Abstract Because the in-control distribution and parameters are generally unknown, control limits have to be estimated using a Phase I reference sample. Because different practitioners obtain different samples, their control limit estimates will vary and, consequently, also their control chart performance. We propose the use of nonparametric tolerance intervals in statistical process monitoring to

Repair delays are common in systems where failures are not self-revealed or in mission-critical systems where repairs cannot be conducted during a mission. The presence of repair delays may significantly affect the failure behavior of a system, but its impact has been largely overlooked in the literature. In this article, we propose a flexible intensity-based model for repairable systems subject to

(2020). Information Quality: The Potential of Data and Analytics to Generate Knowledge. Journal of Quality Technology: Vol. 52, No. 4, pp. 425-426.

(2020). Storytelling with Data: A Data Visualization Guide for Business Professionals. Journal of Quality Technology: Vol. 52, No. 4, pp. 422-422.

(2020). Statistical Intervals: A Guide for Practitioners and Researchers. Journal of Quality Technology: Vol. 52, No. 3, pp. 323-324.