Search

In some manufacturing processes, the quality of a product can be characterized by the functional relationship between the response variable and the explanatory variables. Profile monitoring is a tool used to examine the stability of the functional relationship over time. Many studies have pointed out that the measurements within a profile are uncorrelated. In this research, to take advantage of the ability to provide a comprehensive description of a wide variety of data, the Tweedie exponential dispersion process model is used to account for the within-profile correlation. Then, two exponentially weighted moving average control charts are developed to detect process changes during a Phase II application. In the simulation studies, we showed that our proposed control charts outperform the existing methods for monitoring linear and nonlinear profiles. Finally, two numerical examples are given to demonstrate the usefulness of our proposed control charts. [ABSTRACT FROM AUTHOR]

In today's manufacturing industries, if the quality characteristic of a product or a process is assumed to be represented by a functional relationship between the response variable and one or more explanatory variables, then the data generated from such a relationship are called profile data. Generally speaking, the functional relationship of the profile data rarely occurs in linear form, and the real data usually do not follow normal distribution. Thus, in this paper, the functional relationship of profile data is described via a nonparametric regression model and a nonparametric exponentially weighted moving average (EWMA) control chart is developed for detecting the process shifts for nonlinear profile data in the Phase II monitoring. We first fit the nonlinear profile data via a support vector regression model and use the fitted values to calculate the five metrics. Then, the nonparametric EWMA control chart with the five metrics can be constructed accordingly. Moreover, a simulation study is conducted to evaluate the detecting performance of the new control chart under various process shifts using the out‐of‐control average run length. Finally, a realistic nonlinear profile example is used to demonstrate the usefulness of our proposed nonparametric EWMA control chart and its monitoring schemes. It is expected that the proposed nonparametric EWMA control chart can enhance the monitoring efficiency for nonlinear profile data in the phase II study. [ABSTRACT FROM AUTHOR]

Power/sample size (power) analysis estimates the likelihood of successfully finding the statistical significance in a data set. There has been a growing recognition of the importance of power analysis in the proper design of experiments. Power analysis is complex, yet necessary for the success of large studies. It is important to design a study that produces statistically accurate and reliable results. Power computation methods have been well established for both microarray-based gene expression studies and genotyping microarray-based genome-wide association studies. High-throughput sequencing (HTS) has greatly enhanced our ability to conduct biomedical studies at the highest possible resolution (per nucleotide). However, the complexity of power computations is much greater for sequencing data than for the simpler genotyping array data. Research on methods of power computations for HTS-based studies has been recently conducted but is not yet well known or widely used. In this article, we describe the power computation methods that are currently available for a range of HTS-based studies, including DNA sequencing, RNA-sequencing, microbiome sequencing and chromatin immunoprecipitation sequencing. Most importantly, we review the methods of power analysis for several types of sequencing data and guide the reader to the relevant methods for each data type. [ABSTRACT FROM AUTHOR]

In some applications, the quality of the process or product is characterized and summarized by a functional relationship between a response variable and one or more explanatory variables. Profile monitoring is a technique for checking the stability of the relationship over time. Existing linear profile monitoring methods usually assumed the error distribution to be normal. However, this assumption may not always be true in practice. To address this situation, we propose a method for profile monitoring under the framework of generalized linear models when the relationship between the mean and variance of the response variable is known. Two multivariate exponentially weighted moving average control schemes are proposed based on the estimated profile parameters obtained using a quasi-likelihood approach. The performance of the proposed methods is evaluated by simulation studies. Furthermore, the proposed method is applied to a real data set, and the R code for profile monitoring is made available to users. [ABSTRACT FROM AUTHOR]

Purpose – In the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption. However, this assumption may not be true in most practical situations. Thus, the purpose of this paper is to develop new capability indices for evaluating the performance of multivariate processes subject to non-normal distributions. Design/methodology/approach – In this paper, the authors propose three non-normal multivariate process capability indices (MPCIs) RNMCp, RNMCpm and RNMCpu by relieving the normality assumption. Using the two normal MPCIs proposed by Pan and Lee, a weighted standard deviation method (WSD) is used to modify the NMCp and NMCpm indices for the-nominal-the-best case. Then the WSD method is applied to modify the multivariate ND index established by Niverthi and Dey for the-smaller-the-better case. Findings – A simulation study compares the performance of the various multivariate indices. Simulation results show that the actual non-conforming rates can be correctly reflected by the proposed capability indices. The numerical example further demonstrates that the actual quality performance of a non-normal multivariate process can properly reflected by the proposed capability indices. Practical implications – Process capability index is an important SPC tool for measuring the process performance. If the non-normal process data are mistreated as a normal one, it will result in an improper decision and thereby lead to an unnecessary quality loss. The new indices can provide practicing managers and engineers with a better decision-making tool for correctly measuring the performance for any multivariate process or environmental system. Originality/value – Once the existing multivariate quality/environmental problems and their Key Performance Indicators are identified, one may apply the new capability indices to evaluate the performance of various multivariate processes subject to non-normal distributions. [ABSTRACT FROM AUTHOR]

The Shewhart-type control chart is traditionally developed under the normality assumption. In practice, however, this assumption may not hold. Because the skew normal distribution represents a broad distribution class and is more flexible than is the normal distribution, we propose two new control charts to monitor process mean and spread for skew normal distributed data. Moreover, to facilitate practical implementation, tables of charting constants are provided. We conducted simulation studies to compare the false alarm rates, and the results show that new proposed charts perform better than others as skewness increases. Finally, an illustrative example is provided. [ABSTRACT FROM PUBLISHER]