Relevant bibliographies by topics / Shewhart's control charts

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  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Shewhart's control charts'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Shewhart's control charts"

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BALKIN, SANDY D., and DENNIS K. J. LIN. "PERFORMANCE OF SENSITIZING RULES ON SHEWHART CONTROL CHARTS WITH AUTOCORRELATED DATA." International Journal of Reliability, Quality and Safety Engineering 08, no. 02 (June 2001): 159–71. http://dx.doi.org/10.1142/s0218539301000438.

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Sensitizing Rules are commonly applied to Shewhart Charts to increase their effectiveness in detecting shifts in the mean that may otherwise go unnoticed by the usual "out-of-control" signals. The purpose of this paper is to demonstrate how well these rules actually perform when the data exhibit autocorrelation compared to non-correlated data. Since most control chart data are collected as time series, it is of interest to examine the performance of Shewhart's [Formula: see text] Chart using data generated from typical time series models. In this paper, measurements arising from autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA) processes are examined using Shewhart Control Charts in conjunction with several sensitizing rules. The results indicate that the rules work well when there are strong autocorrelative relationships, but are not as effective in recognizing small to moderate levels of correlation. We conclude with the recommendation to practitioners that they use a more definitive measure of autocorrelation such as the Sample Autocorrelation Function correlogram to detect dependency.
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Adams, Benjamin M. "Advanced Topics in Statistical Process Control: The Power of Shewhart's Charts." Technometrics 38, no. 3 (August 1996): 286. http://dx.doi.org/10.1080/00401706.1996.10484510.

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Lovelace, Cynthia R. "Advanced Topics in Statistical Process Control: The Power of Shewhart's Charts." Journal of Quality Technology 28, no. 1 (January 1996): 127. http://dx.doi.org/10.1080/00224065.1996.11979644.

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Czabak-Górska, Izabela Dagmara. "THE CLASSIFICATION AND CHARACTERISTICS OF CONTROL CHARTS." CBU International Conference Proceedings 5 (September 22, 2017): 86–93. http://dx.doi.org/10.12955/cbup.v5.907.

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Control Charts are the basic tool for quality control. They were developed in the 1920s when the dominant type of production was mass production. In order to properly use classic Control Charts, the data from the manufacturing process should meet the following assumptions: an empirical distribution of measurement data should be normally distributed or close to a normal distribution, measurement data should be independent, the manufacturing process should be capable of quality depending on the type of Control Chart, a sample that is large enough (sometimes made of several elements) must be taken. Currently, a shift can be observed from mass production towards short production runs, which causes the proper use of the traditional approach to be impossible. In recent years, control charts are once again in the spotlight, and consequently many scientists, i.e. Reynolds, Zimmer, Costa, Calvin and Chan have undertaken the task to adapt the classic idea of keeping Control Charts to modern conditions of production. The development of science in this area allows for the avoidance of making major mistakes in the conduct of Control Charts and for making the wrong decisions based on erroneous analysis. However, the appearance of new literature pieces implies the need to classify Control Charts, therefore, this article describes the idea of conduct, the most important assumptions and distribution of classical Shewhart's Control Charts, as well as a suggestion for the distribution of advanced Control Charts that meet the needs of the currently used production types. The work also contains a concise description of the chosen control charts as well as the threats resulting from their inappropriate selection. This elaboration is an extension to the article of Czabak-Górska (2017).
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Chesher, D., and L. Burnett. "Using Shewhart p control charts of external quality-assurance program data to monitor analytical performance of a clinical chemistry laboratory." Clinical Chemistry 42, no. 9 (September 1, 1996): 1478–82. http://dx.doi.org/10.1093/clinchem/42.9.1478.

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Abstract We have investigated the application of Shewhart's p control charts in our external quality-assurance program to monitor the long-term performance of our laboratory's analytical quality. The p control charts have been able to detect long-term changes in our laboratory's analytical performance that would have been difficult to detect by more-conventional techniques. We have explored methods for interpreting these charts as well as some of their limitations, which include minimum subgroup size and dependence on constant specification limits. These charts may be not only a simple method for the long-term monitoring of analytical performance of a laboratory, but also of use to the organizers of external quality-assurance programs.
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Avakh Darestani, Soroush, Azam Moradi Tadi, Somayeh Taheri, and Maryam Raeiszadeh. "Development of fuzzy U control chart for monitoring defects." International Journal of Quality & Reliability Management 31, no. 7 (July 29, 2014): 811–21. http://dx.doi.org/10.1108/ijqrm-03-2013-0048.

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Purpose – Shewhart's control charts are the most important statistical process control tools that play a role in inspecting and producing quality control. The purpose of this paper is to investigate the attributes of fuzzy U control chart. Design/methodology/approach – If the data were uncertain, they were converted into trapezoidal fuzzy number and the fuzzy upper and lower control limits were trapezoidal fuzzy number calculated using fuzzy mode approach. The result was grouped into four categories (in control, out of control, rather in control, rather out of control). Finally, a case study was presented and the method coding was done in MATLAB software using design U control chart; then, the results were verified. Findings – The definition of fuzzy numbers for each type of defect sensitivity and the unit can be classified into four groups: in-control and out-of-control, rather in-control and rather out-of-control which represent the actual quality of the products. It can be concluded that fuzzy control chart is more sensitive on recognition out of control patterns. Originality/value – This paper studies the use of control charts, specifically the attributes of a fuzzy U control chart, for monitoring defects in the format of a case study.
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Orssatto, Fábio, Marcio A. Vilas Boas, Ricardo Nagamine, and Miguel A. Uribe-Opazo. "Shewhart's control charts and process capability ratio applied to a sewage treatment station." Engenharia Agrícola 34, no. 4 (August 2014): 770–79. http://dx.doi.org/10.1590/s0100-69162014000400016.

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The current study used statistical methods of quality control to evaluate the performance of a sewage treatment station. The concerned station is located in Cascavel city, Paraná State. The evaluated parameters were hydrogenionic potential, settleable solids, total suspended solids, chemical oxygen demand and biochemical oxygen demand in five days. Statistical analysis was performed through Shewhart control charts and process capability ratio. According to Shewhart charts, only the BOD(5.20) variable was under statistical control. Through capability ratios, we observed that except for pH the sewage treatment station is not capable to produce effluents under characteristics that fulfill specifications or standard launching required by environmental legislation.
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Bachtiak-Radka, Emilia, Sara Dudzińska, and Daniel Grochała. "Application of Shewhart's control card to supervise the quality of manufacturing process in the automotive industry." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 19, no. 9 (September 30, 2018): 108–11. http://dx.doi.org/10.24136/atest.2018.295.

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The manuscript presents the use of Shewhart control charts to supervision the quality of production processes in the automotive industry of the gearbox automatic. At the request of the company, control charts were prepared for individual characteristic geometrical features. This paper presents the procedure for preparing the control card for monitoring the shape characteristics in an automated production socket.
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DELGADO, M., P. OLAVARRIETA, and P. VERGARA. "FUZZY SET BASED PROTOCOLS FOR PROCESS QUALITY CONTROL." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, no. 01 (February 2006): 61–76. http://dx.doi.org/10.1142/s0218488506003832.

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Industrial process quality control has as yet been carried out using Shewhart's classic charts and control charts with probabilistic limits, using sampling statistics for average and deviation [Formula: see text] and [Formula: see text], respectively, or Cp and Cpk, derived from them, in order to determine whether the process is precise or imprecise. Although these statistics has been formulated using crisp mathematics, their use returns statements about "quality control" which are full of vagueness (for example, the aforementioned idea of precise or imprecise processes). For this reason, it seems both natural and interesting to introduce tools from Fuzzy Sets Theory for the formulation of quality control models. Fuzzy Sets shall be used to study process quality capability and to generate a bilateral simultaneous control for the central tendency and a unilateral one for variability. We shall define linguistic rules in order to perform this control and membership functions for the sample control mean and deviation, [Formula: see text] and ŝ.
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Sparks, Ross S., and John B. F. Field. "Using Deming's Funnel Experiment to Demonstrate Effects of Violating Assumptions Underlying Shewhart's Control Charts." American Statistician 54, no. 4 (November 2000): 291. http://dx.doi.org/10.2307/2685781.

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Dissertations / Theses on the topic "Shewhart's control charts"

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Korzenowski, Andre Luis. "Premissas e suposições para construção de gráficos de controle : um framework para verificação." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/17143.

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O presente trabalho propõe um framework que inclui a organização de procedimentos e técnicas estatísticas para a verificação da premissa e suposições dos gráficos de controle. Ao final do framework o usuário tem a indicação de qual gráfico é mais propício a condição dos dados em relação as suposições verificadas. O método é dividido em 4 fases que engloba a verificação da premissa de estacionariedade e das suposições de normalidade, independência e homocedasticidade. Procedimentos com o objetivo de adequar os dados as suposições são apresentados. Esta dissertação apresenta sugestões para solução dos problemas relacionados a violação da suposição de homocedasticidade. Descreve os principais modelos de obtenção de resíduos independentes e normal e identicamente distribuídos como solução para a violação de independência. São efetuados dois estudos de simulação Monte Carlo onde, como principais resultados, obteve-se: (i) um procedimento eficiente para verificação da premissa de que o processo encontra-se sob controle antes da implantação dos gráficos de controle e; (ii) o efeito da não normalidade na probabilidade de erros do tipo I nos gráficos X e S de Shewhart. Além disso, apresenta a relação entre tamanho de amostra e não normalidade como aspecto importante na construção de gráficos de controle do tipo X e S de Shewhart em relação ao erro do tipo I.
This paper proposes a framework that includes the organization of procedures and statistical techniques for the verification of the control chart's premise and assumptions. At the end of the framework is an indication of which chart has more favorable data condition on assumptions noted. The method is divided into 4 phases which includes verification of the stationarity premise and assumptions of normality, independence and homoscedasticity. Procedures with the goal of matching the data were been presented. This Master's work presents suggestions for solving problems related to violation of the homoscedasticity assumption. Describes the main types of models to intend get normal independent and identically distributed residuals as a solution to the violation of assumptions in the original data. Two studies are performed in Monte Carlo simulation and the main results obtained is: (i) an efficient procedure for verifying the premise that the process is under control before the implantation of control charts, (ii) the effect of non-normality in the probability of Type I error in and S Shewhart's control charts. In addition, shows the relationship between sample size and non-normality as important factor in building and S Shewhart's control charts on the error of Type I.
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Širjovová, Zuzana. "Statistické zpracování dat o zmetkovitosti reálného procesu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2011. http://www.nusl.cz/ntk/nusl-229693.

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Quality, as well as stability of processes check is nowadays gaining on its significance. The main driving force of its increasing importance is the rapid expansion of series production. Large-scale manufacturing processes are concerned, in terms of the number of operators, direct and indirect influence on rejects, but also on process stability. There are several quality characteristics, defined by utility properties, that can be easily measurable (linear dimensions, solidity, elongation, humidity, concentration) or directly immeasurable, mostly subjective (fragrance, taste, color, comfort while using, appearance). Statistical Process Control-SPC features the preventive tool of quality control, because based on the early detection of significant divergences of the process from the predetermined level, it is possible to execute interventions in the process with the aim of maintaining the acceptable and stable level and improving the process. This manufacturing process check will be the topic of my thesis. At first, Shewhart's control charts analysis will be done to determine strong process instability. Consequently, analysis will be carried out in test chi-square, used to determine the influence of each factor on the process of stability (operators, type of shift, type of product, type of defect on the product). All the practical part will be processed in statistical software MINITAB15. The thesis will be complemented by the findings from the examination of the features of interval estimates of parameter p for binomial distribution - numeric stability in the various statistical software (specifically Minitab 15, Statistica, Matlab 7.8.0). Master's thesis was supported by project from MSMT of the Czech Republic no. 1M06047 Center for Quality and Reliability of Production.
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Zou, Xueli. "A robust Shewhart control chart adjustment strategy." Diss., This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-06062008-164701/.

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Hughes, Christopher Scott. "Variable Sampling Rate Control Charts for Monitoring Process Variance." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/37643.

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Industrial processes are subject to changes that can adversely affect product quality. A change in the process that increases the variability of the output of the process causes the output to be less uniform and increases the probability that individual items will not meet specifications. Statistical control charts for monitoring process variance can be used to detect an increase in the variability of the output of a process so that the situation can be repaired and product uniformity restored. Control charts that increase the sampling rate when there is evidence the variance has changed gather information more quickly and detect changes in the variance more quickly (on average) than fixed sampling rate procedures. Several variable sampling rate procedures for detecting increases in the process variance will be developed and compared with fixed sampling rate methods. A control chart for the variance is usually used with a separate control chart for the mean so that changes in the average level of the process and the variability of the process can both be detected. A simple method for applying variable sampling rate techniques to dual monitoring of mean and variance will be developed. This control chart procedure increases the sampling rate when there is evidence the mean or variance has changed so that changes in either parameter that will negatively impact product quality will be detected quickly.
Ph. D.
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Hall, Deborah A. "A comparison of alternative methods to the shewhart-type control chart." Thesis, Virginia Tech, 1989. http://hdl.handle.net/10919/44642.

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A control chart that simultaneously tracks the mean and variance of a normally distributed variable with no compensation effect is defined in this work. This joint control chart is compared to five other charts: an Χ chart, an s2 chart, a Reynolds and Ghosh chart, a Repko process capability plot, and a t-statistic chart. The criterion for comparison is the probability of a Type II sampling error. Several out-of-control cases are examined. In the case of Repko, an equation is defined to compute the Type II error probability. The results indicate that the Reynolds and Ghosh statistic is powerful for cases when the variance shifts out of control. The Χ chart is powerful when the mean shifts with moderate changes in the variance. The joint chart is powerful for moderate changes in the mean and variance.
Master of Science

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Yi, Junsub. "Comparisons of Neural Networks, Shewhart ‾x, and CUSUM Control Charts Under the Condition of Nonnormality." Thesis, University of North Texas, 1997. https://digital.library.unt.edu/ark:/67531/metadc277797/.

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In this study, neural networks are developed under conditions of nonnormality as alternatives to standard control charts, and their performance is compared with those of standard ‾x and CUSUM control charts.
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Vining, G. Geoffrey. "Determining the most appropiate [sic] sampling interval for a Shewhart X-chart." Thesis, Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/94487.

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A common problem encountered in practice is determining when it is appropriate to change the sampling interval for control charts. This thesis examines this problem for Shewhart X̅ charts. Duncan's economic model (1956) is used to develop a relationship between the most appropriate sampling interval and the present rate of"disturbances,” where a disturbance is a shift to an out of control state. A procedure is proposed which switches the interval to convenient values whenever a shift in the rate of disturbances is detected. An example using simulation demonstrates the procedure.
M.S.
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Nam, Kyungdoo T. "A Heuristic Procedure for Specifying Parameters in Neural Network Models for Shewhart X-bar Control Chart Applications." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278815/.

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This study develops a heuristic procedure for specifying parameters for a neural network configuration (learning rate, momentum, and the number of neurons in a single hidden layer) in Shewhart X-bar control chart applications. Also, this study examines the replicability of the neural network solution when the neural network is retrained several times with different initial weights.
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Huang, Wandi. "GLR Control Charts for Monitoring a Proportion." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/40405.

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The generalized likelihood ratio (GLR) control charts are studied for monitoring a process proportion of defective or nonconforming items. The type of process change considered is an abrupt sustained increase in the process proportion, which implies deterioration of the process quality. The objective is to effectively detect a wide range of shift sizes. For the first part of this research, we assume samples are collected using rational subgrouping with sample size n>1, and the binomial GLR statistic is constructed based on a moving window of past sample statistics that follow a binomial distribution. Steady state performance is evaluated for the binomial GLR chart and the other widely used binomial charts. We find that in terms of the overall performance, the binomial GLR chart is at least as good as the other charts. In addition, since it has only two charting parameters that both can be easily obtained based on the approach we propose, less effort is required to design the binomial GLR chart for practical applications. The second part of this research develops a Bernoulli GLR chart to monitor processes based on the continuous inspection, in which case samples of size n=1 are observed. A constant upper bound is imposed on the estimate of the process shift, preventing the corresponding Bernoulli GLR statistic from being undefined. Performance comparisons between the Bernoulli GLR chart and the other charts show that the Bernoulli GLR chart has better overall performance than its competitors, especially for detecting small shifts.
Ph. D.
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Graham, Marien Alet. "Contributions to the theory and applications of univariate distribution-free Shewhart, CUSUM and EWMA control charts." Thesis, University of Pretoria, 2013. http://hdl.handle.net/2263/32971.

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Distribution-free (nonparametric) control charts can be useful to the quality practitioner when the underlying distribution is not known. The term nonparametric is not intended to imply that there are no parameters involved, in fact, quite the contrary. While the term distribution-free seems to be a better description of what we expect from these charts, that is, they remain valid for a large class of distributions, nonparametric is perhaps the term more often used. In the statistics literature there is now a rather vast collection of nonparametric tests and confidence intervals and these methods have been shown to perform well compared to their normal theory counterparts. Remarkably, even when the underlying distribution is normal, the efficiency of some nonparametric tests relative to the corresponding (optimal) normal theory methods can be as high as 0.955 (see e.g. Gibbons and Chakraborti (2010) page 218). For some other heavy-tailed and skewed distributions, the efficiency can be 1.0 or even higher. It may be argued that nonparametric methods will be ‘less efficient’ than their parametric counterparts when one has a complete knowledge of the process distribution for which that parametric method was specifically designed. However, the reality is that such information is seldom, if ever, available in practice. Thus it seems natural to develop and use nonparametric methods in statistical process control (SPC) and the quality practitioners will be well advised to have these techniques in their toolkits. In this thesis we only propose univariate nonparametric control charts designed to track the location of a continuous process since very few charts are available for monitoring the scale and simultaneously monitoring the location and scale of a process. Chapter 1 gives a brief introduction to SPC and provides background information regarding the research conducted in this thesis. This will aid in familiarizing the reader with concepts and terminology that are helpful to the following chapters. Details are given regarding the three main classes of control charts, namely the Shewhart chart, the cumulative sum (CUSUM) chart and the exponentially weighted moving average (EWMA) chart. We begin Chapter 2 with a literature overview of Shewhart-type Phase I control charts followed by the design and implementation of these charts. A nonparametric Shewhart-type Phase I control chart for monitoring the location of a continuous variable is proposed. The chart is based on the pooled median of the available Phase I samples and the charting statistics are the counts (number of observations) in each sample that are less than the pooled median. The derivations recognize that in Phase I the signalling events are dependent and that more than one comparison is © University of Pretoria v made against the same estimated limits simultaneously; this leads to working with the joint distribution of a set of dependant random variables. An exact expression for the false alarm probability is given in terms of the multivariate hypergeometric distribution and this is used to provide tables for the control limits. Some approximations are discussed in terms of the univariate hypergeometric and the normal distributions. In Chapter 3 Phase II control charts are introduced and considered for the case when the underlying parameters of the process distribution are known or specified. This is referred to as the ‘standard(s) known’ case and is denoted Case K. Two nonparametric Phase II control charts are considered in this chapter, with the first one being a nonparametric exponentially weighted moving average (NPEWMA)-type control chart based on the sign (SN) statistic. A Markov chain approach (see e.g. Fu and Lou (2003)) is used to determine the run-length distribution of the chart and some associated performance characteristics (such as the average, standard deviation, median and other percentiles). In order to aid practical implementation, tables are provided for the chart’s design parameters. An extensive simulation study shows that on the basis of minimal required assumptions, robustness of the in-control run-length distribution and out-of-control performance, the proposed NPEWMA-SN chart can be a strong contender in many applications where traditional parametric charts are currently used. Secondly, we consider the NPEWMA chart that was introduced by Amin and Searcy (1991) using the Wilcoxon signed-rank statistic (see e.g. Gibbons and Chakraborti (2010) page 195). This is called the nonparametric exponentially weighted moving average signed-rank (NPEWMA-SR) chart. In their article important questions remained unanswered regarding the practical implementation as well as the performance of this chart. In this thesis we address these issues with a more in-depth study of the NPEWMA-SR chart. A Markov chain approach is used to compute the run-length distribution and the associated performance characteristics. Detailed guidelines and recommendations for selecting the chart’s design parameters for practical implementation are provided along with illustrative examples. An extensive simulation study is done on the performance of the chart including a detailed comparison with a number of existing control charts. Results show that the NPEWMA-SR chart performs just as well as and in some cases better than the competitors. In Chapter 4 Phase II control charts are introduced and considered for the case when the underlying parameters of the process distribution are unknown and need to be estimated. This is referred to as the ‘standard(s) unknown’ case and is denoted Case U. Two nonparametric Phase II control charts are proposed in this chapter. They are a Phase II NPEWMA-type control chart and a nonparametric cumulative sum (NPCUSUM)-type control chart, based on the exceedance statistics, © University of Pretoria vi respectively, for detecting a shift in the location parameter of a continuous distribution. The exceedance statistics can be more efficient than rank-based methods when the underlying distribution is heavy-tailed and / or right-skewed, which may be the case in some applications, particularly with certain lifetime data. Moreover, exceedance statistics can save testing time and resources as they can be applied as soon as a certain order statistic of the reference sample is available. We also investigate the choice of the order statistics (percentile), from the reference (Phase I) sample that defines the exceedance statistic. It is observed that other choices, such as the third quartile, can play an important role in improving the performance of these exceedance charts. It is seen that these exceedance charts perform as well as and, in many cases, better than its competitors and thus can be a useful alternative chart in practice. Chapter 5 wraps up this thesis with a summary of the research carried out and offers concluding remarks concerning unanswered questions and / or future research opportunities. © University
Thesis (PhD)--University of Pretoria, 2013.
gm2013
Statistics
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Books on the topic "Shewhart's control charts"

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Advanced topics in statistical process control: The power of Shewhart's charts. Knoxville, Tenn: SPC Press, 1995.

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Wheeler, Donald J. The Power of Shewhart's Charts: The Power of Shewhart's Charts. SPC Press, Inc., 1995.

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Wheeler, Donald J. Advanced Topics in Statistical Process Control: The Power of Shewhart's Charts. 2nd ed. Spc Pr, 2004.

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Roes, C. B. Shewhart-Type Charts in Statistical Process Control. I.B.D. Limited, 1995.

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Book chapters on the topic "Shewhart's control charts"

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Kahraman, Cengiz, Murat Gülbay, and Eda Boltürk. "Fuzzy Shewhart Control Charts." In Fuzzy Statistical Decision-Making, 263–80. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39014-7_14.

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Giacalone, Massimiliano. "Shewhart’s Control Chart: Some Observations." In Studies in Classification, Data Analysis, and Knowledge Organization, 295–302. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60126-2_37.

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Wetherill, G. Barrie, and Don W. Brown. "Basic Shewhart control charts for continuous variables." In Statistical Process Control, 85–113. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-2949-5_5.

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Wetherill, G. Barrie, and Don W. Brown. "Extensions to Shewhart charts for one-at-a-time data." In Statistical Process Control, 114–37. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-2949-5_6.

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Knoth, Sven. "New Results for Two-Sided CUSUM-Shewhart Control Charts." In Frontiers in Statistical Quality Control 12, 45–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75295-2_3.

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Hart, M. K., and R. F. Hart. "Shewhart Control Charts for Individuals with Time-Ordered Data." In Frontiers in Statistical Quality Control 4, 123–37. Heidelberg: Physica-Verlag HD, 1992. http://dx.doi.org/10.1007/978-3-662-11789-7_9.

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Goedhart, Rob. "Design Considerations and Trade-offs for Shewhart Control Charts." In Frontiers in Statistical Quality Control 13, 13–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67856-2_2.

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Koutras, Markos V., and Ioannis S. Triantafyllou. "Recent Advances on Univariate Distribution-Free Shewhart-Type Control Charts." In Distribution-Free Methods for Statistical Process Monitoring and Control, 1–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-25081-2_1.

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Castagliola, Philippe, Kim Phuc Tran, Giovanni Celano, and Petros E. Maravelakis. "The Shewhart Sign Chart with Ties: Performance and Alternatives." In Distribution-Free Methods for Statistical Process Monitoring and Control, 107–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-25081-2_3.

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Knoth, Sven, Wolfgang Schmid, and Alexander Schöne. "Simultaneous Shewhart-Type Charts for the Mean and the Variance of a Time Series." In Frontiers in Statistical Quality Control 6, 61–79. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-642-57590-7_5.

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Conference papers on the topic "Shewhart's control charts"

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Dubinin, N. N., V. V. Kalinin, A. V. Kokovin, O. S. Guseva, S. N. Lapshina, A. Dolganov, and S. S. Parusheva. "Shewhart’s control charts in the education quality management system." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5044045.

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Sheriff, M. Ziyan, and Mohamed N. Nounou. "Enhanced performance of shewhart charts using multiscale representation." In 2016 American Control Conference (ACC). IEEE, 2016. http://dx.doi.org/10.1109/acc.2016.7526763.

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Sheriff, M. Ziyan, Fouzi Harrou, and Mohamed Nounou. "Univariate process monitoring using multiscale Shewhart charts." In 2014 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2014. http://dx.doi.org/10.1109/codit.2014.6996933.

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Brun, Alessandro, and Galia Novakova. "Evaluation of Adjustment Strategies for Adaptive Shewhart Quality Control Charts." In International Body Engineering Conference & Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2003. http://dx.doi.org/10.4271/2003-01-2836.

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Lesso, Igor, Pavel Horovcak, Beata Stehlikova, Zuzana Gasparova, and Patrik Flegner. "The multidimensional Shewhart control chart for multicriteria quality management." In 2015 16th International Carpathian Control Conference (ICCC). IEEE, 2015. http://dx.doi.org/10.1109/carpathiancc.2015.7145090.

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Lin, Tse-Chieh. "Composite Shewhart-Poisson GWMA Control Chart with Sensitizing Rules." In 2008 Eighth International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2008. http://dx.doi.org/10.1109/isda.2008.328.

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Jamali, Abdul, Li JinLin, and Muhammad Durad. "Average Run Length Performance of Shewhart Control Charts with Interpretation Rules." In 2006 IEEE International Conference on Industrial Informatics. IEEE, 2006. http://dx.doi.org/10.1109/indin.2006.275852.

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Nidsunkid, S., J. J. Borkowski, and K. Budsaba. "The effects of violations of assumptions in multivariate Shewhart control charts." In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2016. http://dx.doi.org/10.1109/ieem.2016.7797867.

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Chang, Shing I. "Approaches to Implement Statistical Process Control for Manufacturing in Big Data Era." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2840.

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Abstract:
It has been long overdue to revamp existing statistical process control (SPC) approaches in manufacturing since Water Shewhart first proposed the use of control charts in 1924. The combination development of big data, cloud computing, and manufacturing reshoring back to Untied States has opened up the opportunities to rethink implementation strategies of SPC for manufacturing. This paper first reviews the history of SPC development in traditional manufacturing environments and then contrasts it with the opportunities presented in big data era. Five SPC implementation approaches are proposed based on the opportunities identified.
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Djauhari, Maman A., Rohayu Mohd Salleh, Zunnaaim Zolkeply, and Lee Siaw Li. "On the reliability of Shewhart-type control charts for multivariate process variability." In THE 3RD ISM INTERNATIONAL STATISTICAL CONFERENCE 2016 (ISM-III): Bringing Professionalism and Prestige in Statistics. Author(s), 2017. http://dx.doi.org/10.1063/1.4982866.

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