Monitoring process mean and variability with generally weighted moving average control charts

doi:10.1016/J.CIE.2008.12.010

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Monitoring process mean and variability with generally weighted moving average control charts

Journal Article•DOI•

Monitoring process mean and variability with generally weighted moving average control charts

Shey-Huei Sheu¹, Shih-Hung Tai², Yu-Tai Hsieh¹, Tse-Chieh Lin²•Institutions (2)

National Taiwan University of Science and Technology¹, Lunghwa University of Science and Technology²

01 Aug 2009-Computers & Industrial Engineering (Pergamon)-Vol. 57, Iss: 1, pp 401-407

TL;DR: It is shown that the combination of the generally weighted moving average (GWMA) mean chart and a two-side GWMA variance chart is more sensitive than the combined EWMA charts for detecting small shifts in the process mean and variance.

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About: This article is published in Computers & Industrial Engineering.The article was published on 2009-08-01. It has received 37 citations till now. The article focuses on the topics: EWMA chart & Control chart.

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Journal Article•DOI•

Statistical Quality Control

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Amit Mitra

01 Nov 2002-Technometrics

TL;DR: Stuart Coles’s book on the modeling of extreme values provides an introductory text on the topic, a modeling-oriented text with an emphasis on different types of data and analytical approaches, meant for individuals with moderate statistical background.

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Abstract: The modeling of extreme values is important to scientists in such elds as hydrology, civil engineering, environmental science, oceanography and nance. Stuart Coles’s book on the modeling of extreme values provides an introductory text on the topic. It is a modeling-oriented text with an emphasis on different types of data and analytical approaches. The book is laid out in nine chapters. Following introductory material and discussion of necessary theoretical background are chapters on approaches to extreme values that focus on the different types of data that might be used in an extreme value analysis. These include models for block maximums, threshold models, models for data from stationary and nonstationary processes, and approaches based on point processes. A chapter covers analysis of multivariate extremes, and the nal chapter brie y covers such topics as Bayesian inference, Markov chains, and spatial extremes. Although this is not a data-driven text, it does contain numerous examples and analyses. These examples are used to illustrate the methodology; I would have preferred to see more motivation and interpretation of the results of the analyses. Datasets and S-PLUS programs for the analyses in the text are available at a website. These are easy to use for those slightly familiar with S-PLUS. The appendix describes the programs and illustrates how to access data and use the programs. It also gives links to sites that provide other software. The text does not include problem sets; these would have been useful, especially if the text is to be used in coursework. The text by Reiss and Thomas (2001) contains more thoroughly analyzed datasets, although it is twice the length and not as streamlined as the text under review. The book is meant for individuals with moderate statistical background. Those with coursework in maximum likelihood methods should have no dif culty reading and comprehending the text. Overall, this is a good text for someone getting started in extreme value methods.

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402 citations

Journal Article•DOI•

On efficient use of auxiliary information for control charting in SPC

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Shabbir Ahmad¹, Saddam Akber Abbasi², Muhammad Riaz², Nasir Abbas³•Institutions (3)

COMSATS Institute of Information Technology¹, King Fahd University of Petroleum and Minerals², University of Sargodha³

01 Jan 2014-Computers & Industrial Engineering

TL;DR: This study deals with the Shewhart type variability control charts based on auxiliary characteristics for the non-cascading processes, assuming stability of auxiliary parameters.

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62 citations

Journal Article•DOI•

Process monitoring and fault detection strategies: a review

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Anupam Das¹, Jhareswar Maiti, Rupendranath Banerjee•Institutions (1)

Sikkim Manipal University¹

27 Jul 2012-International Journal of Quality & Reliability Management

TL;DR: A review of PMFD strategies can be found in this paper, where a taxonomy was developed to categorize, describe, and compare the various PMFD techniques and their application in diversified fields.

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Abstract: Purpose – Monitoring of a process leading to the detection of faults and determination of the root causes are essential for the production of consistent good quality end products with improved yield. The history of process monitoring fault detection (PMFD) strategies can be traced back to 1930s. Thereafter various tools, techniques and approaches were developed along with their application in diversified fields. The purpose of this paper is to make a review to categorize, describe and compare the various PMFD strategies.Design/methodology/approach – Taxonomy was developed to categorize PMFD strategies. The basis for the categorization was the type of techniques being employed for devising the PMFD strategies. Further, PMFD strategies were discussed in detail along with emphasis on the areas of applications. Comparative evaluations of the PMFD strategies based on some commonly identified issues were also carried out. A general framework common to all the PMFD has been presented. And lastly a discussion int...

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46 citations

Journal Article•DOI•

An EWMA monitoring scheme with a single auxiliary variable for industrial processes

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Ridwan A. Sanusi¹, Muhammad Riaz², Nurudeen A. Adegoke³, Min Xie¹•Institutions (3)

City University of Hong Kong¹, King Fahd University of Petroleum and Minerals², Massey University³

01 Dec 2017-Computers & Industrial Engineering

TL;DR: An EWMA-type scheme, with a single auxiliary variable, is proposed to improve the performance of the classical EWMA chart for monitoring location parameter and outperforms its existing counterparts.

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39 citations

Cites background from "Monitoring process mean and variabi..."

...For more works on the improvement of EWMA chart, interested readers can see Liu et al. (2007), Sheu et al. (2009), Teh et al. (2011), Xie et al. (2011), Nishimura et al. (2015), and Zwetsloot et al. (2016)....
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Journal Article•DOI•

Monitoring Process Mean and Variance with a Single Generally Weighted Moving Average Chart

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Sin Yin Teh¹, Michael B. C. Khoo¹, Zhang Wu²•Institutions (2)

Universiti Sains Malaysia¹, Nanyang Technological University²

09 May 2012-Communications in Statistics-theory and Methods

TL;DR: The Max-GWMA chart as mentioned in this paper is based on the maximum of the absolute values of two weighted moving average (GWMA) statistics, one for controlling the mean and the other the variance, and it outperforms the combined GWMA chart in terms of the average run length, standard deviation of the run length (SDRL), and diagnostic abilities performances.

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Abstract: Two generally weighted moving average (GWMA) charts are usually used concurrently for a simultaneous monitoring of the process mean and process variance. In this article, we propose a new GWMA chart, called the Max-GWMA chart, which uses a single statistic for a simultaneous monitoring of the process mean and variance. The statistic of the Max-GWMA chart is based on the maximum of the absolute values of two GWMA statistics, one for controlling the mean while the other the variance. We show that the Max-GWMA chart outperforms the combined GWMA chart, in terms of the average run length (ARL), standard deviation of the run length (SDRL) and diagnostic abilities performances. The combined GWMA chart consists of two GWMA charts that are run concurrently, one for monitoring the mean and the other the variance.

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25 citations

Cites background from "Monitoring process mean and variabi..."

...It is worth noting the interesting relationships between the GWMA, EWMA, and Shewhart X charts (Sheu et al., 2009)....
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References

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Book•

Introduction to Statistical Quality Control

[...]

Douglas C. Montgomery

01 Jan 1985

TL;DR: In this article, the authors present a survey of statistical process control and capability analysis techniques for improving the quality of a business process in the modern business environment, using a variety of techniques.

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Abstract: Quality Improvement in the Modern Business Environment.STAISTICAL METHODS USEFUL IN QUALITY IMPROVEMENT.Modeling Process Quality.Inferences About Process Quality.BASIC METHODS OF STATISTICAL PROCESS CONTROL AND CAPABILITY ANALYSIS.Methods and Philosophy of Statistical Process Control.Control Charts for Variables.Control Charts for Attributes.Process and Measurement Systems System Capability Analysis.OTHER STATISTICAL PROCESS MONITORING AND CONTROL TECHNIQUES.Cumulative Sum and Exponentially Weighted Moving Average Control Charts.Other Univariate SPC Techniques.Multivariate Process Monitoring and Control.Engineering Process Control and SPC.PROCESS DESIGN AND IMPROVEMENT WITH DESIGNED EXPERIMENTS.Factorial and Fractional Factorial Designs for Process Design and Improvement.Process Optimization with Designed Experiments.ACCEPTANCE SAMPLING.Lot--by--Lot Acceptance Sampling for Attributes.Other Acceptance Sampling Techniques.Appendix.Bibliography.Answers to Selected Exercises.Index.

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7,312 citations

Journal Article•

Exponentially weighted moving average control schemes: Properties and enhancements

[...]

James M. Lucas, Michael S. Saccucci

01 Jan 1991-Quality Engineering

TL;DR: The recognition that an EWMA control scheme can be represented as a Markov chain allows its properties to be evaluated more easily and completely than has previously been done.

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1,624 citations

"Monitoring process mean and variabi..." refers methods in this paper

...Nevertheless, the ARL can be obtained through numerical analysis and computer simulation (Crowder, 1987a; Crowder, 1987b; Crowder, 1987c; Gan, 1991; Lucas & Saccucci, 1990; Roberts, 1959; Robinson & Ho, 1978)....
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Journal Article•DOI•

Control chart tests based on geometric moving averages

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S. W. Roberts¹•Institutions (1)

Bell Labs¹

01 Feb 2000-Technometrics

TL;DR: In this article, a graphical procedure for generating geometric moving averages is described in which the most recent observation is assigned a weight r, and all previous observations weights decreasing in geometric progression from the most recently back to the first.

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Abstract: A geometrical moving average gives the most recent observation the greatest weight, and all previous observations weights decreasing in geometric progression from the most recent back to the first. A graphical procedure for generating geometric moving averages is described in which the most recent observation is assigned a weight r. The properties of control chart tests based on geometric moving averages are compared to tests based on ordinary moving averages.

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1,490 citations

Journal Article•DOI•

Exponentially weighted moving average control schemes: properties and enhancements

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James M. Lucas¹, Michael S. Saccucci², Robert V. Baxley, William H. Woodall, Hazem D. Maragh, Fedrick W. Faltin, Gerald J. Hahn, William T. Tucker, J. Stuart Hunter, John F. MacGregor, Thomas J. Harris - Show less +7 more•Institutions (2)

DuPont¹, Drexel University²

03 Jan 1990-Technometrics

TL;DR: In this article, the authors evaluate the properties of an exponentially weighted moving average (EWMA) control scheme used to monitor the mean of a normally distributed process that may experience shifts away from the target value.

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Abstract: Roberts (1959) first introduced the exponentially weighted moving average (EWMA) control scheme. Using simulation to evaluate its properties, he showed that the EWMA is useful for detecting small shifts in the mean of a process. The recognition that an EWMA control scheme can be represented as a Markov chain allows its properties to be evaluated more easily and completely than has previously been done. In this article, we evaluate the properties of an EWMA control scheme used to monitor the mean of a normally distributed process that may experience shifts away from the target value. A design procedure for EWMA control schemes is given. Parameter values not commonly used in the literature are shown to be useful for detecting small shifts in a process. In addition, several enhancements to EWMA control schemes are considered. These include a fast initial response feature that makes the EWMA control scheme more sensitive to start-up problems, a combined Shewhart EWMA that provides protection against both larg...

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1,380 citations

Journal Article•DOI•

Introduction to Statistical Quality Control (2nd ed.)

[...]

Roger M. Sauter¹•Institutions (1)

Boeing Commercial Airplanes¹

01 May 1992-Technometrics

1,264 citations

"Monitoring process mean and variabi..." refers background or methods in this paper

...The control chart technique has been widely applied in manufacturing industries because its chart is easy to plot, easy to interpret, and its control limits are easy to obtain (DeVor, Chang, & Sutherland, 1992; Duncan, 1986; Gitlow, 1995; Grant & Leavenworth, 1988; Mitra, 1993; Montgomery, 2001)....
[...]
...Montgomery (2001) pointed out that in computer integrated manufacturing where sensors are used to measure every unit manufactured, the subgroup size was set to n = 1. Control charts are basic and powerful tools in statistical process control and are widely used to control various industrial processes. It is very important that control charts can quickly detect the out-of-control signals when the process mean shifts. The method of Sheu and Griffith (1996) and Sheu (1998), Sheu (1999) is applied to EWMA control charts to enhance the detection ability of control charts. The expanded chart of Sheu and Lin (2003) is called the generally weighted moving average (GWMA) control chart. Their simulation results indicated that GWMA is more sensitive than EWMA in detecting small shifts in the process mean. Sweet (1986) recommended using two EWMA charts, one to detect mean shifts and the other to detect changes in variance....
[...]
...Montgomery (2001) pointed out that in computer integrated manufacturing where sensors are used to measure every unit manufactured, the subgroup size was set to n = 1. Control charts are basic and powerful tools in statistical process control and are widely used to control various industrial processes. It is very important that control charts can quickly detect the out-of-control signals when the process mean shifts. The method of Sheu and Griffith (1996) and Sheu (1998), Sheu (1999) is applied to EWMA control charts to enhance the detection ability of control charts. The expanded chart of Sheu and Lin (2003) is called the generally weighted moving average (GWMA) control chart....
[...]
...Montgomery (2001) pointed out that in computer integrated manufacturing where sensors are used to measure every unit manufactured, the subgroup size was set to n = 1. Control charts are basic and powerful tools in statistical process control and are widely used to control various industrial processes. It is very important that control charts can quickly detect the out-of-control signals when the process mean shifts. The method of Sheu and Griffith (1996) and Sheu (1998), Sheu (1999) is applied to EWMA control charts to enhance the detection ability of control charts. The expanded chart of Sheu and Lin (2003) is called the generally weighted moving average (GWMA) control chart. Their simulation results indicated that GWMA is more sensitive than EWMA in detecting small shifts in the process mean. Sweet (1986) recommended using two EWMA charts, one to detect mean shifts and the other to detect changes in variance. Reynolds and Stoumbos (2001) considered the use of two EWMA charts to make individual observations....
[...]
...Montgomery (2001) pointed out that in computer integrated manufacturing where sensors are used to measure every unit manufactured, the subgroup size was set to n = 1. Control charts are basic and powerful tools in statistical process control and are widely used to control various industrial processes. It is very important that control charts can quickly detect the out-of-control signals when the process mean shifts. The method of Sheu and Griffith (1996) and Sheu (1998), Sheu (1999) is applied to EWMA control charts to enhance the detection ability of control charts....
[...]