Joint monitoring of mean and variance using likelihood ratio test statistic with measurement error
04 Mar 2021-Quality Technology and Quantitative Management (Informa UK Limited)-Vol. 18, Iss: 2, pp 202-224
TL;DR: In this article, the effect of measurement errors on a joint monitoring control chart by using three different techniques (i) covariate method (ii) multiple measurements (iii) linear increasing) was explored.
Abstract: This paper explores the effect of measurement errors on a joint monitoring control chart by using three different techniques (i) covariate method (ii) multiple measurements (iii) linear increasing
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TL;DR: In this paper, a control chart is used to monitor longitudinal measurements of a quality characteristic and detect undesirable causes of variability, such as measurement error may exist and deteriorate the quality characteristic.
Abstract: A control chart is used to monitor longitudinal measurements of a quality characteristic and detects undesirable causes of variability. In practice, measurement error may exist and deteriorate the ...
5 citations
TL;DR: In this paper , the authors explored the impact of measurement system inability on sensitivity of MHWMA control chart in Phase II monitoring of the mean vector of multivariate normally distributed quality characteristics.
Abstract: Multivariate control charts are useful tools for monitoring the product quality in which the process outcome is expressed by several correlated variables. The multivariate homogeneously weighted moving average (MHWMA) control chart is one of the most efficient extensions of the multivariate exponentially weighted moving average (MEWMA) procedure. However, this control chart has been proposed under the assumption of precise measurements. This paper explores the impact of the measurement system inability on sensitivity of MHWMA control chart in Phase II monitoring of the mean vector of multivariate normally distributed quality characteristics. Through simulation studies, the run length (RL) properties of MHWMA chart in the presence of measurement errors are investigated and compared to without-error scenario. The results indicate that the imprecise measurements degrade the detection ability of MHWMA control chart in detecting different process disturbances. The undesired impact of gauge measurement errors increases as the error variance increases. Moreover, multiple measurements on each sampled item are utilized to compensate for the undesired effect of the measurement errors on run length characteristics of the MHWMA control chart. It is also concluded that, as the number of measurements on each sampled point increases, the detection capability of MHWMA chart reduces and approaches the without-error case. Finally, a realistic illustrative example from a healthcare system is utilized to elaborate on the impact of imprecise measurements on chart performance.
4 citations
TL;DR: In this article , a detailed review paper for simultaneous monitoring is conducted in 2013 which many reaserchers were attracted to publish papers in this field, and a meticulous content analysis (on the basis of 59 reviewed papers in the field of joint monitoring from 2013 to 2021) is exploited to classify the papers that includes joint control charts for statistical process monitoring (SPM), to identify the potential topics and present some suggestions for further studies in simultaneous monitoring.
Abstract: Process monitoring is regarded as a continuous phenomenon requiring careful consideration to acquire an enhanced output quality. Dispersion and location are significant parameters in the entire process, and timely detection of the changes that occur in a stable process is needed. Today, quality practitioners recommend using a single charting setup offering better capability of detecting joint changes in the parameters of a process. As provided in the literature, a detailed review paper for simultaneous monitoring is conducted in 2013 which many reaserchers were attracted to publish papers in this field. In this paper, a meticulous content analysis (on the basis of 59 reviewed papers in the field of joint monitoring from 2013 to 2021) is exploited to classify the papers that includes joint control charts for statistical process monitoring (SPM), to identify the potential topics and present some suggestions for further studies in simultaneous monitoring.
4 citations
TL;DR: In this article, an attempt is made to develop a new control chart that integrates the exponentially weighted moving average (AWA) with the quality control chart, which is used as a process monitoring tool.
Abstract: Quality control charts are widely used as a process monitoring tool. In this article, an attempt is made to develop a new control chart that integrates the exponentially weighted moving average pro...
4 citations
References
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Book•
05 Jun 2009
TL;DR: This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book.
Abstract: This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book. It covers the fundamental theories in linear regression analysis and is extremely useful for future research in this area. The examples of regression analysis using the Statistical Application System (SAS) are also included. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject fields. Introduction Simple Linear Regression Multiple Linear Regression Detection of Outliers and Influential Observations in Multiple Linear Regression Model Selection Model Diagnostics Extensions of Least Squares Generalized Linear Models Bayesian Linear Regression
609 citations
TL;DR: In this paper, the authors present a broad overview of generic methods for estimating the capability and designed expertise of a quality engineering system. But they focus on three categories of methods: basic methods, generic methods, and designed methods.
Abstract: (1993). GAUGE CAPABILITY AND DESIGNED EXPERIMENTS. PART I: BASIC METHODS. Quality Engineering: Vol. 6, No. 1, pp. 115-135.
192 citations
TL;DR: Significant measurement error often exists in quality control applications as mentioned in this paper, which is known to result in reduced power to detect a given change in the mean or variance of a quality chara...
Abstract: Significant measurement error often exists in quality control applications. Measurement error is known to result in reduced power to detect a given change in the mean or variance of a quality chara...
141 citations
TL;DR: In this paper, a model demonstrates that the performance of multivariate control charting methods based on measured covariates is not directionally invariant to shifts in the mean vector of the underlying process variables.
Abstract: A model demonstrates that the performance of multivariate control charting methods based on measured covariates is not directionally invariant to shifts in the mean vector of the underlying process variables. This is the case even if it is directionally..
92 citations
TL;DR: This work builds-up the samples with non-neighbouring items, according to the time they were produced, to counteract the undesired effect of autocorrelation.
Abstract: Measurement error and autocorrelation often exist in quality control applications. Both have an adverse effect on the X¯ chart's performance. To counteract the undesired effect of autocorrelation, we build-up the samples with non-neighbouring items, according to the time they were produced. To counteract the undesired effect of measurement error, we measure the quality characteristic of each item of the sample several times. The chart's performance is assessed when multiple measurements are applied and the samples are built by taking one item from the production line and skipping one, two or more before selecting the next.
90 citations