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One of the basic assumptions for traditional univariate and multivariate control charts is that the data are independent in time. For the latter, in many cases, the data are serially dependent (autocorrelated) and cross-correlated because of, for example, frequent sampling and process dynamics. It is well known that the autocorrelation affects the false alarm rate and the shift-detection ability of the traditional univariate control charts. However, how the false alarm rate and the shiftdetection ability of the Hotelling T-2 control chart are affected by various autocorrelation and cross- correlation structures for different magnitudes of shifts in the process mean is not fully explored in the literature. In this article, the performance of the Hotelling T-2 control chart for different shift sizes and various autocorrelation and cross- correlation structures are compared based on the average run length using simulated data. Three different approaches in constructing the Hotelling T-2 chart are studied for two different estimates of the covariance matrix: (i) ignoring the autocorrelation and using the raw data with theoretical upper control limits; (ii) ignoring the autocorrelation and using the raw data with adjusted control limits calculated through Monte Carlo simulations; and (iii) constructing the control chart for the residuals from a multivariate time series model fitted to the raw data. To limit the complexity, we use a first-order vector autoregressive process and focus mainly on bivariate data. (c) 2014 The Authors. Quality and Reliability Engineering International published by John Wiley & Sons Ltd.

A basic assumption when using principal component analysis (PCA) for inferential purposes, such as in statistical process control (SPC), is that the data are independent in time. In many industrial processes, frequent sampling and process dynamics make this assumption unrealistic rendering sampled data autocorrelated (serially dependent). PCA can be used to reduce data dimensionality and to simplify multivariate SPC. Although there have been some attempts in the literature to deal with autocorrelated data in PCA, we argue that the impact of autocorrelation on PCA and PCA-based SPC is neither well understood nor properly documented. This article illustrates through simulations the impact of autocorrelation on the descriptive ability of PCA and on the monitoring performance using PCA-based SPC when autocorrelation is ignored. In the simulations, cross-correlated and autocorrelated data are generated using a stationary first-order vector autoregressive model. The results show that the descriptive ability of PCA may be seriously affected by autocorrelation causing a need to incorporate additional principal components to maintain the model's explanatory ability. When all variables have equal coefficients in a diagonal autoregressive coefficient matrix, the descriptive ability is intact, while a significant impact occurs when the variables have different degrees of autocorrelation. We also illustrate that autocorrelation may impact PCA-based SPC and cause lower false alarm rates and delayed shift detection, especially for negative autocorrelation. However, for larger shifts, the impact of autocorrelation seems rather small.

The origins of quality engineering are in manufacturing, where quality engineers apply basic statistical methodologies to improve the quality and productivity of products and processes. In the past decade, people have discovered that these methodologies are effective for improving almost any type of system or process, such as financial, health care, and supply chains. This paper begins with a review of key advances and trends within quality engineering over the past decade. The second part uses the first part as a foundation to outline new application areas for the field. It also discusses how quality engineering needs to evolve in order to make significant contributions to these new areas. © 2015 The Authors Quality and Reliability Engineering International Published by John Wiley & Sons Ltd.

The Comet assay is a sensitive technique for detection of DNA strand breaks. The experimental design of in vivo Comet assay studies are often hierarchically structured, which should be reWected in the statistical analysis. However, the hierarchical structure sometimes seems to be disregarded, and this imposes considerable impact on the type I error rate. This study aims to demonstrate the implications that result from disregarding the hierarchical structure. DiUerent combinations of the factor levels as they appear in a literature study give type I error rates up to 0.51 and for all combinations the type I error rate is greater than the nominal _ at 0.05. Closed-form expressions based on scaled F-distributions using the Welch-Satterthwaite approximation are provided to show how the type I error rate is aUected. With this study we hope to motivate researchers to be more precise regarding the exposition of the statistical methodology and to suitably account for the hierarchical structure of Comet assay data whenever present.

We propose a new approach for monitoring animal movement in thermal videos. The method distinguishes movements as walking in the expected direction from walking in the opposite direction, stopping or lying down. The method utilizes blob detection combined with opti-cal ow to segment the pigs and extract features which characterize a pig’s movement (direction and speed). Subsequently a multiway princi-pal component analysis is used to analyze the movement features and monitor their development over time. Results are presented in the form of quality control charts of the principal components. The method works on-line with pre-training.

As sensor and computer technology continues to improve, it becomes a normal occurrence that we confront with high dimensional data sets. As in many areas of industrial statistics, this brings forth various challenges in statistical process control (SPC) and monitoring for which the aim is to identify “out-of-control” state of a process using control charts in order to reduce the excessive variation caused by so-called assignable causes. In practice, the most common method of monitoring multivariate data is through a statistic akin to the Hotelling’s T2. For high dimensional data with excessive amount of cross correlation, practitioners are often recommended to use latent structures methods such as Principal Component Analysis to summarize the data in only a few linear combinations of the original variables that capture most of the variation in the data. Applications of these control charts in conjunction with image data are plagued with various challenges beyond the usual ones encountered in current applications. In this presentation we will introduce the basic ideas of SPC and the multivariate control charts commonly used in industry. We will further discuss the challenges the practitioners are facing with in the implementation of these charts.

In many industrial applications, autocorrelated data are becoming increasingly common due to, for example, on-line data collection systems with high-frequency sampling. Therefore, the basic assumption of independent observations for process capability analysis is not valid. The purpose of this article is to compare decision methods using the process capability index C-pk when data are autocorrelated. This is done through a case study followed by a simulation study. In the simulation study the actual significance level and power of the decision methods are investigated. The outcome of the article is that two methods appeared to be better than the others.

In this article we investigate two-level split-plot designs where the sub-plots consist of only two mirror image trials. Assuming third and higher order interactions negligible, we show that these designs divide the estimated effects into two orthogonal sub-spaces, separating sub-plot main effects and sub-plot by whole-plot interactions from the rest. Further we show how to construct split-plot designs of projectivity P≥3. We also introduce a new class of split-plot designs with mirror image pairs constructed from non-geometric Plackett–Burman designs. The design properties of such designs are very appealing with effects of major interest free from full aliasing assuming that 3rd and higher order interactions are negligible.

Split-plot designs are commonly used in industrial experiments when there are hard-to-change and easy-to-change factors. Due to the number of factors and resource limitations, it is more practical to run a fractional factorial split-plot (FFSP) design. These designs are variations of the fractional factorial (FF) design, with the restricted randomisation structure to account for the whole plots and subplots. We discuss the formulation of FFSP designs using integer programming (IP) to achieve various design criteria. We specifically look at the maximum number of clear two-factor interactions and variations on this criterion.

As sensor and computer technology continues to improve, it becomes a normal occurrence that we confront with high dimensional data sets. As in many areas of industrial statistics, this brings forth various challenges in statistical process control and monitoring. This new high dimensional data often exhibit not only cross-­‐correlation among the quality characteristics of interest but also serial dependence as a consequence of high sampling frequency and system dynamics. In practice, the most common method of monitoring multivariate data is through what is called the Hotelling’s T2 statistic. For high dimensional data with excessive amount of cross correlation, practitioners are often recommended to use latent structures methods such as Principal Component Analysis to summarize the data in only a few linear combinations of the original variables that capture most of the variation in the data. In this paper, we discuss the effect of autocorrelation (when it is ignored) on multivariate control charts based on these methods and provide some practical suggestions and remedies to overcome this problem.

The simultaneous optimization of multiple responses in a dynamic system is challenging. When a response has a known gradient, it is often easily improved along the path of steepest ascent. On the contrary, a stochastic approximation technique may be used when the gradient is unknown or costly to obtain. We consider the problem of optimizing multiple responses in which the gradient is known for only one response. We propose a hybrid approach for this problem, called simultaneous perturbation stochastic approximation steepest ascent, SPSA-SA or SP(SA)(2) for short. SP(SA)(2) is an SPSA technique that leverages information about the known gradient to constrain the perturbations used to approximate the others. We apply SP(SA)(2) to the cross-layer optimization of throughput, packet loss, and end-to-end delay in a mobile ad hoc network (MANET), a self-organizing wireless network. The results show that SP(SA)(2) achieves higher throughput and lower packet loss and end-to-end delay than the steepest ascent, SPSA, and the Nelder-Mead stochastic approximation approaches. It also reduces the cost in the number of iterations to perform the optimization.

Summary form given only:In this article experiments on a discrete event simulation model for an orthopedic surgery are considered. The model is developed as part of a larger project in co-operation with Copenhagen University Hospital in Gentofte. Experiments on the model are performed by using Latin hypercube designs. The parameter set consists of system settings such as use of preparation room for sedation and the number of operating rooms, as well as management decisions such as staffing, size of the recovery room and the number of simultaneously active operating rooms. Sensitivity analysis and optimization combined with meta-modeling are employed in search for optimal setups. The primary objective in this article is to minimize time spent by the patients in the system. The overall long-term objective for the orthopedic surgery unit is to minimize time lost during the pre- and post operation activities for acute and elective surgery as well as dedicated elective surgery.

In many experimental situations, it may not be feasible or even possible to run experiments in a completely randomized fashion as usually recommended. Under these circumstances, split-plot experiments in which certain factors are changed less frequently than the others are often used. Most of the literature on split-plot designs is based on 2-level factorials. For those designs, the number of subplots is a power of 2. There may however be some situations where for cost purposes or physical constraints, we may need to have unusual number of subplots such as 3, 5, 6, etc. In this article, we explore this issue and provide some examples based on the Plackett and Burman designs. Also algorithmically constructed D-optimal split-plot designs are compared to those based on Plackett and Burman designs.

Maintaining optimal performance as the conditions of a system change is a challenging problem. To solve this problem, we present meta-regression, a general methodology for alleviating traditional difficulties in nonlinear regression modelling. Meta-regression allows for reactive optimization, in which system components self-organize to changing conditions in a manner that is robust, or affected minimally by other sources of variability. Meta-regression extends profiling, providing a methodology for model-building when there is incomplete knowledge of the mechanisms and interactions of a nonlinear system.