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Wilson, Sara R. "Control Charts with Missing Observations." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/37623.
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Traditional control charts for process monitoring are based on taking samples from the process at regular time intervals. However, it is often possible in practice for observations, and even entire samples, to be missing. This dissertation investigates missing observations in Exponentially Weighted Moving Average (EWMA) and Multivariate EWMA (MEWMA) control charts. The standardized sample mean is used since this adjusts the sample mean for the fact that part of the sample may be missing. It also allows for constant control limits even though the sample size varies randomly. When complete samples are missing, the weights between samples should also be adjusted.
In the univariate case, three approaches for adjusting the weights of the EWMA control statistic are investigated: (1) ignoring missing samples; (2) adding the weights from previous consecutive missing samples to the current sample; and (3) increasing the weights of non-missing samples in proportion, so that the weights sum to one. Integral equation and Markov chain methods are developed to find and compare the statistical properties of these charts. The EI chart, which adjusts the weights by ignoring the missing samples, has the best overall performance.
The multivariate case in which information on some of the variables is missing is also examined using MEWMA charts. Two methods for adjusting the weights of the MEWMA control statistic are investigated and compared using simulation: (1) ignoring all the data at a sampling point if the data for at least one variable is missing; and (2) using the previous EWMA value for any variable for which all the data are missing. Both of these methods are examined when the in-control covariance matrix is adjusted at each sampling point to account for missing observations, and when it is not adjusted. The MS control chart, which uses the previous value of the EWMA statistic for a variable if all of the data for that variable is missing at a sampling point, provides the best overall performance. The in-control covariance matrix needs to be adjusted at each sampling point, unless the variables are independent or only weakly correlated.Ph. D.
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VanBrackle, Lewis N. "EWMA and CUSUM control charts in the presence of correlation." Diss., This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-07282008-134346/.
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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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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
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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.
© UniversityThesis (PhD)--University of Pretoria, 2013.
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Xu, Liaosa. "The Design of GLR Control Charts for Process Monitoring." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/50408.
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Generalized likelihood ratio (GLR) control charts are investigated for two types of statistical process monitoring (SPC) problems. The first part of this dissertation considers the problem of monitoring a normally distributed process variable when a special cause may produce a time varying linear drift in the mean. The design and application of a GLR control chart for drift detection is investigated. The GLR drift chart does not require specification of any tuning parameters by the practitioner, and has the advantage that, at the time of the signal, estimates of both the change point and the drift rate are immediately available. An equation is provided to accurately approximate the control limit. The performance of the GLR drift chart is compared to other control charts such as a standard CUSUM chart and a CUSCORE chart designed for drift detection. We also compare the GLR chart designed for drift detection to the GLR chart designed for sustained shift detection since both of them require only a control limit to be specified. In terms of the expected time for detection and in terms of the bias and mean squared error of the change-point estimators, the GLR drift chart has better performance for a wide range of drift rates relative to the GLR shift chart when the out-of-control process is truly a linear drift.
The second part of the dissertation considers the problem of monitoring a linear functional relationship between a response variable and one or more explanatory variables (a linear profile). The design and application of GLR control charts for this problem are investigated. The likelihood ratio test of the GLR chart is generalized over the regression coefficients, the variance of the error term, and the possible change-point. The performance of the GLR chart is compared to various existing control charts. We show that the overall performance of the GLR chart is much better than other options in detecting a wide range of shift sizes. The existing control charts designed for certain shifts that may be of particular interest have several chart parameters that need to be specified by the user, which makes the design of such control charts more difficult. The GLR chart is very simple to design, as it is invariant to the choice of design matrix and the values of in-control parameters. Therefore there is only one design parameter (the control limit) that needs to be specified. Especially, the GLR chart can be constructed based on the sample size of n=1 at each sampling point, whereas other charts cannot be applied. Another advantage of the GLR chart is its built-in diagnostic aids that provide estimates of both the change-point and the values of linear profile parameters.
Ph. D.
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Albarracin, Orlando Yesid Esparza. "Generalized autoregressive and moving average models: control charts, multicollinearity, and a new modified model." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-21112017-184544/.
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Recently, in the health surveillance area, control charts have been proposed to decide if the morbidity or mortality of a specific disease reached an epidemic level. This thesis is composed by 3 papers. In the first two papers, CUSUM and EWMA control charts were proposed to monitor count time series with seasonal and trend effects using the Generalized Autoregressive and Moving Average models (GARMA), instead of the independent Generalized Linear Model (GLM) as it is usually used in practice. Different statistics based on transformations, for variables that follow a Negative Binomial distribution, were used in these control charts. In the second paper, two new statistics were proposed based on the ratio of log-likelihood function. Different scenarios describing disease profiles were considered to evaluate the effect of omission of serial correlation in EWMA and CUSUM control charts. The performance of CUSUM and EWMA charts when the serial correlation is neglected in the regression model was measure in terms of average run length (ARL). In summary, when the autocorrelation is neglected, fitting a pure GLM instead of a GARMA model will lead to an increase of false alarms. However, no statistics among the tested ones seem to be robust, in a sense to produce the smallest increase of false alarms in all scenarios. In general, all monitored statistics presented a smaller ARL_0 for higher values of autocorrelation. \\\\ In the last paper, the GARMA models (p, q) with p and q simultaneously different from zero were studied since that two features were observed in practice. One is the multicollinearity, which may lead to a non-convergence of the maximum likelihood, using iteratively reweighted least squares. The second is the inclusion of the same lagged observations into the autoregressive and moving average components confounding the interpretation of the parameters. In a general sense, simulation studies show that the modified model provide estimators closer to the parameters and offer confidence intervals with higher coverage percentage than obtained with the GARMA model, but some restrictions in the parametric space are imposed to guarantee the stationarity of the process. Also, a real data analysis illustrate the GARMA-M fit for daily hospilatization rates of elderly people due to respiratory diseases from October 2012 to April 2015 in São Paulo city, Brazil.Recentemente, no campo da saúde, gráficos de controle têm sido propostos para monitorar a morbidade ou a mortalidade decorrentes de doenças. Este trabalho está composto por três artigos. Nos dois primeiros artigos, gráficos de controle CUSUM e EWMA foram propostos para monitorar séries temporais de contagens com efeitos sazonais e de tendência usando os modelos Generalized autoregressive and moving average models (GARMA), em vez dos modelos lineares generalizados (GLM), como usualmente são utilizados na prática. Diferentes estatísticas baseadas em transformações, para variávies que seguem uma distribuição Binomial Negativa, foram usadas nestes gráficos de controle. No segundo artigo foram propostas duas novas estatísticas baseadas na razão da função de log-verossimilhança. Diferentes cenários que descrevem perfis de doenças foram considerados para avaliar o efeito da omissão da correlação serial nesses gráficos de controle. Este impacto foi medido em termos do Average Run Lenght (ARL). Notou-se que a negligência da correlação serial induz um aumento de falsos alarmes. Em geral, todas as estatísticas monitoradas apresentaram menores valores de ARL_0 para maiores valores de autocorrelação. No entanto, nenhuma estatística entre as consideradas mostrou ser mais robusta, no sentido de produzir o menor aumento de falsos alarmes nos cenários considerados. No último artigo, foram estudados os modelos GARMA (p, q) com p e q simultaneamente diferentes de zero, uma vez que duas características foram observadas na prática. A primeira é a presença de multicolinearidade, que induz à não-convergência do método de máxima verossimilhança usando mínimos quadrados ponderados reiterados. A segunda é a inclusão dos mesmos termos defasados nos componentes autorregressivos e de médias móveis. Um modelo modificado, GARMA-M, foi apresentado para lidar com a multicolinearidade e melhorar a interpretação dos parâmetros. Em sentido geral, estudos de simulação mostraram que o modelo modificado fornece estimativas mais próximas dos parâmetros e intervalos de confiança com uma cobertura percentual maior do que a obtida nos modelos GARMA. No entanto, algumas restrições no espaço paramétrico são impostas para garantir a estacionariedade do processo. Por último, uma análise de dados reais ilustra o ajuste do modelo GARMA-M para o número de internações diárias de idosos devido a doenças respiratórias de outubro de 2012 a abril de 2015 na cidade de São Paulo, Brasil.
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Wetherington, Les O. "Evaluation of CUSUM and EWMA control charts to detect changes in underlying demand trends of naval aviation spares." Thesis, Monterey, California. Naval Postgraduate School, 2010. http://hdl.handle.net/10945/5177.
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Approved for public release; distribution is unlimitedThe Navy must keep aircraft in a high state of readiness around the globe requiring spare parts to be available when and where needed. Managers need to know when changes in demand patterns are occurring far enough in advance to ensure continued availability of needed spare parts. This thesis presents an evaluation of two techniques using widely available software operating in a Windows environment to determine if changes are occurring in underlying demand patterns. These techniques are Cumulative Sum Control Charting and Exponentially Weighted Moving Average Control Charting. The use of the techniques was validated using a computer generated data set with known variation characteristics, and related processes were developed. After validation, the techniques were applied to four actual data sets with demand information from Navy aircraft. Both techniques proved effective with Cumulative Sum Charting providing slightly earlier alarms, and Exponentially Weighted Moving Averages being easier to use. Use of these techniques could allow detection of changes in time to mitigate the negative effects of the change and could be applied to a very wide range of processes. For the Navy, the widespread use of these techniques could lead to more aircraft being available for combat missions.
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Šváchová, Mariana. "Určování způsobilosti a stability vybraného technického procesu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-417798.
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This diploma thesis deals with the evaluation of the capability of a specific production process. The theoretical part of the work contains a description of statistical process control, types of control charts and evaluation of process capability. The practical part is focused on evaluating the capability of a specific process. The method of dataset collection is described at first, then this data are analyzed and the capability of this process is evaluated.
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Pehlivan, Canan. "Controlling High Quality Manufacturing Processes: A Robustness Study Of The Lower-sided Tbe Ewma Procedure." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609937/index.pdf.
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In quality control applications, Time-Between-Events (TBE) type observations may be monitored by using Exponentially Weighted Moving Average (EWMA) control charts. A widely accepted model for the TBE processes is the exponential
distribution, and hence TBE EWMA charts are designed under this assumption. Nevertheless, practical applications do not always conform to the theory and it is common that the observations do not fit the exponential model. Therefore, control charts that are robust to departures from the assumed distribution are desirable in practice. In this thesis, robustness of the lower-sided TBE EWMA charts to the assumption of exponentially distributed observations has been investigated. Weibull and lognormal distributions are considered in order to represent the departures from the assumed exponential model and Markov Chain approach is utilized for evaluating the performance of the chart. By analyzing the performance results, design settings are suggested in order to achieve robust lower-sided TBE EWMA charts.
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Wang, Sai. "GLR Control Charts for Monitoring the Mean Vector or the Dispersion of a Multivariate Normal Process." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77227.
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In many applications, the quality of process outputs is described by more than one characteristic variable. These quality variables usually follow a multivariate normal (MN) distribution. This dissertation discusses the monitoring of the mean vector and the covariance matrix of MN processes.
The first part of this dissertation develops a statistical process control (SPC) chart based on a generalized likelihood ratio (GLR) statistic to monitor the mean vector. The performance of the GLR chart is compared to the performance of the Hotelling Χ² chart, the multivariate exponentially weighted moving average (MEWMA) chart, and a multi-MEWMA combination. Results show that the Hotelling Χ² chart and the MEWMA chart are only effective for a small range of shift sizes in the mean vector, while the GLR chart and some carefully designed multi-MEWMA combinations can give similarly better overall performance in detecting a wide range of shift magnitudes. Unlike most of these other options, the GLR chart does not require specification of tuning parameter values by the user. The GLR chart also has the advantage in process diagnostics: at the time of a signal, estimates of change-point and out-of-control mean vector are immediately available to the user. All these advantages of the GLR chart make it a favorable option for practitioners. For the design of the GLR chart, a series of easy to use equations are provided to users for calculating the control limit to achieve the desired in-control performance. The use of this GLR chart with a variable sampling interval (VSI) scheme has also been evaluated and discussed.
The rest of the dissertation considers the problem of monitoring the covariance matrix. Three GLR charts with different covariance matrix estimators have been discussed. Results show that the GLR chart with a multivariate exponentially weighted moving covariance (MEWMC) matrix estimator is slightly better than the existing method for detecting any general changes in the covariance matrix, and the GLR chart with a constrained maximum likelihood estimator (CMLE) gives much better overall performance for detecting a wide range of shift sizes than the best available options for detecting only variance increases.Ph. D.
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AL, Cihan, and Kubra Koroglu. "Detection of the Change Point and Optimal Stopping Time by Using Control Charts on Energy Derivatives." Thesis, Högskolan i Halmstad, Tillämpad matematik och fysik (MPE-lab), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-17371.
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MENDES, FLAVIA CESAR TEIXEIRA. "EWMA CONTROL CHART FOR NONCONFORMITIES WITH VARIABLE SAMPLING INTERVAL." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2004. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5209@1.
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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOROs gráficos de controle de processo criados por Shewhart na década de 20 e em uso até hoje são eficientes para sinalizar alterações de grande magnitude na característica de qualidade de um processo (por exemplo, desvios da ordem de mais de 2 desvios-padrão, no caso do gráfico de médias); já para alterações de menos magnitude, ele são mais lentos. Para estas últimas, são sabidamente mais eficientes os esquemas CUSUM e EWMA, bem como os gráficos adaptativos, de desenvolvimento bem mais recente, também chamados de gráficos de parâmentros variáveis, porque alguns ou todos os seus parâmetros (tamanho de amostra, intervalo de tempo entre amostras, e limites de controle) passam a variar durante a operação, em função da informação fornecida pela última amostra. Nesta pesquisa, é prposta a incorporação da estratégia de gráficos adaptativos (usando um intervalo de tempo entre amostras variável) ao esquema EWMA na busca de melhorias no desempenho de gráficos de controle por atributos. O esquema proposto é aplicado a gráficos de c para detecção de alterações de pequena magnitude no número médio de não-conformidades em um processo de produção. É desenvolvido o modelo matemático para cálculo das medidas de desempenho do gráfico, e é realizada a análise de desempenho do esquema para diversos valores de c0 e c1 (número médio em controle e fora de controle de não- conformidades), com comparação com outros gráficos de controle por atributos. Resultados mostram, na maioria das situações analisadas, a vantagem do esquema proposto, em termos de uma maior rapidez de detecção de alterações de diversas magnitudes.
The process control charts created by Shewhart in the 20 s and still in use today are efficient in signaling large shifts in the quality characteristics of a process (e.g. shifts greater than two standard deviations, in the case of the chart for means); they are however slower in the case of small and moderate shifts, in which case CUSUM and EWMA schemes are known to be more efficient, as are the recently developed adaptive charts, also called variable parameter charts because some or all of their design parameters (sample size, sampling interval and control limits) are allowed to vary during the operation, according to the information of the latest sample. In this thesis, looking for an enhancement in the performance of control charts for attributes, the strategy of adaptive charts (using a variable sampling interval) is incorporated to the EWMA scheme. The proposed scheme is applied to c charts for detecting small shifts in the number of nonconformities in a production process. A mathematical model is developed for calculation of the performance measures of the chart, and a performance analysis is carried out for several values of c0 and c1 (in- and out-of-control number of nonconformities), together with a comparison with other control charts for nonconformities. The results show the advantage of the proposed scheme in the majority of the analyzed situations, through faster detection of a range of shifts.
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Urbieta, Pablo Cezar. "Gráficos CUSUM e EWMA para monitorar dados de contagem com distribuição binominal negativa." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3136/tde-30092016-143355/.
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Gráficos de controle têm sido amplamente utilizados na manufatura para melhoria de processos. Diversas abordagens tem sido propostas para melhorar o desempenho dos gráficos existentes na literatura. Além disso, o uso de gráficos de controle tem se estendido para outras áreas, tais como, economia, finanças, medicina, etc. O objetivo deste trabalho é comparar o gráfico CUSUM com o gráfico EWMA para monitoramento do número diário de internações hospitalares. Para tanto, utilizou-se uma série histórica de internações devido a doenças respiratórias para a população acima de 65 anos. Um modelo linear foi ajustado considerando que o número de internações segue uma distribuição Binomial Negativa. São simulados diversos cenários de mudança no número médio de internações e utilizando diferentes estatísticas baseadas em transformações, é feita uma comparação entre estes gráficos. Verifica-se que o gráfico EWMA com limite de controle assintótico possui desempenho muito similar ao gráfico CUSUM. Já o EWMA implementado com limite de controle exato apresenta melhor desempenho em relação ao gráfico CUSUM quando se atribui pesos menores aos dados atuais.Control charts have been widely used for process improvement in manufacturing. In literature several approaches have been proposed to improve the current charts performance. In addition, the use of control charts has been extended to other areas such as economics, finance, medicine, and others. The objective of this study is to compare CUSUM control chart with EWMA control chart for monitoring daily number of hospital admissions. Using a historical hospitalizations series due to respiratory diseases for people over 65 years old, a Negative Binomial regression model is fitted. Several scenarios are simulated using different shifts in the mean and using different statistics based on transformations, in order to compare these charts. It is shown that EWMA control chart with asymptotic control limit has similar performance as CUSUM control chart. However, using smaller values for new observations the EWMA control chart with exact control limit has better performance than CUSUM control chart.
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SIMOES, BRUNO FRANCISCO TEIXEIRA. "EWMA CHART WITH ADAPTIVE SMOOTHING CONSTANT FOR STATISTICAL PROCESS CONTROL." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8189@1.
Full textAbstract:
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOREste trabalho propõe um gráfico de controle EWMA para observações individuais ou médias amostrais, com a constante de amortecimento variando entre dois valores de acordo com o valor mais recente da estatística EWMA, para obter detecção mais rápida de alterações pequenas a moderadas na média do processo, e sem a complexidade operacional apresentada por outros esquemas adaptativos, pois o tamanho da amostra e o intervalo de amostragem são mantidos fixos. Já existe um outro trabalho propondo a variação da constante de amortecimento dos gráficos EWMA, mas com base em outro critério: Capizzi e Masarotto (2003). O esquema EWMA adaptativo foi combinado com limites de Shewhart para os valores individuais (ou médias amostrais), para acelerar a detecção de grandes deslocamentos da média do processo, também sem aumento da complexidade operacional. Os NMA1´s - números esperados de amostras até um sinal verdadeiro - foram calculados por um método de aproximação numérica usando um modelo matemático por cadeias de Markov, e comparados com os do esquema EWMA tradicional (com parâmetros fixos) e com os do esquema adaptativo de Capizzi e Masarotto (2003). O esquema proposto tende a fornecer NMA1´s menores para alterações na média acima de 1,0 desvio-padrão, e o esquema de Capizzi e Masarotto (2003) tende a fornecer NMA1´s menores para pequenas alterações. Ambos os esquemas possuem melhor desempenho que o gráfico EWMA com parâmetros fixos. Uma vantagem que pode se tornar decisiva para a adoção do esquema proposto é a simplicidade dos cálculos requeridos para o monitoramento.
This work proposes an EWMA process control chart for individual observations or subgroup averages, in which the smoothing constant varies between two values according to the most recent value of the EWMA statistic, in order to achieve faster detection of small to moderate shifts in the process mean, and without the operational complexities presented by other adaptive schemes, since its sample size and sampling interval do not vary. There is one other work proposing the adaptive variation of the smoothing constant of EWMA charts, but based on a different criterion: Capizzi and Masarotto (2003). The adaptive EWMA scheme was combined with Shewhart limits for the individual values (or subgroup averages), to enhance its sensitivity to large shifts, again with no extra operational burden. The out-of-control average run lengths (ARL1´s) were calculated through a numerical approximation method based on a Markov chain model. The ARL1´s were compared of the proposed scheme, of the traditional (fixed parameter) EWMA chart and of Capizzi and Masarottos´s adaptive EWMA scheme. The proposed scheme generally provides the shortest ARL1´s for shifts in the mean above one standard deviation, and Capizzi and Masarotto´s scheme tends to outperform it for smaller shifts. Both schemes perform better than the fixed parameter EWMA. An advantage that can become decisive for the adoption of the proposed scheme is the simplicity of the calculations required for the monitoring.
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Zheng, Hongzhang. "On Development and Performance Evaluation of Some Biosurveillance Methods." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77128.
Full textAbstract:
This study examines three applications of control charts used for monitoring syndromic data with different characteristics. The first part develops a seasonal autoregressive integrated moving average (SARIMA) based surveillance chart, and compares it with the CDC Early Aberration Reporting System (EARS) W2c method using both authentic and simulated data. After successfully removing the long-term trend and the seasonality involved in syndromic data, the performance of the SARIMA approach is shown to be better than the performance of the EARS method in terms of two key surveillance characteristics, the false alarm rate and the average time to detect the outbreaks.
In the second part, we propose a generalized likelihood ratio (GLR) control chart to detect a wide range of shifts in the mean of Poisson distributed biosurveillance data. The application of a sign function on the original GLR chart statistics leads to downward-sided, upward-sided, and two-sided GLR chart statistics in an unified framework. To facilitate the use of such charts in practice, we provide detailed guidance on developing and implementing the GLR chart. Under the steady-state framework, this study indicates that the overall GLR chart performance in detecting a range of shifts of interest is superior to the performance of traditional control charts including the EARS method, Shewhart charts, EWMA charts, and CUSUM charts.
There is often an excessive number of zeros involved in health care related data. Zero-inflated Poisson (ZIP) models are more appropriate than Poisson models to describe such data. The last part of the dissertation considers the GLR chart for ZIP data under a research framework similar to the second part. Because small sample sizes may influence the estimation of ZIP parameters, the efficiency of MLEs is investigated in depth, followed by suggestions for improvement. Numerical approaches to solving for the MLEs are discussed as well. Statistics for a set of GLR charts are derived, followed by modifications changing them from two-sided statistics to one-sided statistics. Although not a complete study of GLR charts for ZIP processes, due to limited time and resources, suggestions for future work are proposed at the end of this dissertation.Ph. D.
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YEN, WEN-PIN, and 顏文品. "The Three-level EWMA and Shewhart-EWMA Control Charts." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/46615846418699398019.
Full textAbstract:
碩士淡江大學
統計學系碩士班
96
The thesis extends the three-level Shewhart control chart proposed by Cassady and Nachlas [8] to exponentially weighted moving average and Shewhart-EWMA control charts for monitoring the quality of three-level (conforming, marginal, nonconforming) products. The control limits of the proposed control chart are established based on the zero-state average run lengths using Markov chain approximation. Basically, the proposed control charts improve the performance of the three-level Shewhart control chart signi‾cantly and are able to detect small shifts in a process more quickly.
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Graham, Marien Alet. "Theory and applications of univariate distribution-free Shewhart, CUSUM and EWMA control charts." Diss., 2008. http://hdl.handle.net/2263/29585.
Full textAbstract:
Statistical quality control charts originated in the late 1920’s by Shewhart (1926, 1931 and 1939). Their applications in various disciplines have been ever-increasing. Although most control charts are distribution-based, recent literature witnessed the development of a considerable number of distribution-free or nonparametric control charts. The purpose of this thesis is to present the concepts and introduce the researcher to the essentials of univariate nonparametric control charts. Various properties of nonparametric control charts are comprehensively discussed and concepts are clearly explained. Proofs and detailed calculations have been given to help the reader to study and understand the subject more thoroughly. This text contains a wide variety of illustrative examples to give an overall picture of how nonparametric control charts are used. Both simulated and real data examples have been integrated throughout the text. Since most practical problems are too large to be solved using hand calculations, some type of statistical software package is required to solve these problems. There are several excellent statistical packages available and in this thesis we make use of Microsoft Excel, SAS, Minitab, Mathcad and Mathematica to construct (almost all) the tables in this thesis. We point out that a number of Mathematica programs are provided by Chakraborti and Van de Wiel (2003) by means of the website www.win.tue.nl/~markvdw. The aim throughout is to convey the concepts of univariate nonparametric control charts in a way that readers will find attractive and interesting. Since the majority of nonparametric procedures, to be distribution-free, require a continuous population, only variables control charts are covered. We only consider control charts for monitoring the location of a process, since very few nonparametric charts are available for monitoring the spread. In this thesis we consider the three main classes of control charts: the Shewhart, CUSUM and EWMA control charts and their refinements. The text is divided into several chapters. An introduction to nonparametric control charts is presented in Chapter 1. A discussion of some of the advantages of nonparametric control charts is included while pointing out some of the disadvantages. In Chapter 2 we describe the Shewhart-, CUSUMand EWMA-type sign control charts with (and without) warning limits. In Chapter 3 we describe the Shewhart-, CUSUM- and EWMA-type signed-rank control charts with (and without) runs-type signalling rules. The Shewhart-type sign-like control chart with (and without) signalling rules is considered in Chapter 4. In Chapter 5 we consider the Shewharttype signed-rank-like control chart. Finally, in Chapter 6 we consider the Shewhart- and CUSUM-type Mann-Whitney-Wilcoxon control charts. We considered decision problems under both Phase I and Phase II (see Section 1.5 for a distinction between the two phases). In all the sections of this thesis we considered Phase II process monitoring, except in Section 6.2 where a CUSUM-type control chart for the preliminary Phase I analysis of individual observations based on the Mann-Whitney two-sample test is proposed. In the last chapter we have some concluding remarks along with some ideas for future research.Dissertation (MSc)--University of Pretoria, 2011.
Statistics
unrestricted
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KAO, PEI-HSUAN, and 高珮瑄. "The Design of EWMA Control Charts for Multiply Censored Data." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/50581950978740086958.
Full textAbstract:
碩士國立雲林科技大學
工業工程與管理系
104
Due to advances in technology, makes the product life extension, when manufacturers inspection of product life compliance with specifications, considerations on cost and time, often using censored data, but tradition Shewhart control chart monitor censored data, the occurrence of adverse characteristics such as large-alarm rate and low power. Steiner & Mackay (2000) proposed Conditional expected value(CEV) (X ) ̅control charts, to replace traditional (X ) ̅ control charts, the results found in the right type I censored data using CEV control charts better than traditional control charts. Because repair Walter control chart can only detect shift decline, Zhang & Chen (2004) proposed the concept of upper-Side and lower-Side EWMA CEV control charts, the detection process mean increased and decrease. Because research mostly is right type I censored data, multiple censored data is a common data collection methods. In this Study, use of Steiner & Mackay (2000) proposed CEV (X ) ̅ control charts using multiple censored data for monitoring mean decreased of the manufacturing process, combine Zhang & Chen (2004) proposed EWMA control chart. Apply to multiple censored data separately for increasing and decreasing the average process to monitor the situation, and to calculate the ARL1 as control charts to detect performance indicators. The results of this study show Upper and lower EWMA CEV control chart can effectively monitor with the multiple censored data.
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Lin, Yi-Rong, and 林宜蓉. "A comparative study on Shewhart, CUSUM and EWMA attribute control charts." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/57921652374201520795.
Full textAbstract:
碩士國立屏東科技大學
工業管理系所
102
Based on literature review, when the process has a large offsets, Shewhart control chart's detect effects will better than the others. But for today's high-quality products requirement, the detect ability for large offsets has been insufficient to meet the demand. Therefore, when the process has a small offsets, EWMA control chart or CUSUM control chart should be used to monitor the process. When the process is in-control, the average run length is hoped the bigger the better. On the contrary, when the process is out-control, the average run length is hoped the smaller the better. In this paper, we will explore and compare the detecting performance among the Shewhart, CUSUM and EWMA control charts under a variant of quality characteristics. Finally, seven conclusions are drawn for future studies and practical applications.
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Ho, Han-Wei, and 何漢葳. "Effects of Measurement Error on EWMA Control Charts for Two-Step Process." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/35263272984269674635.
Full textAbstract:
碩士國立政治大學
統計研究所
91
In this article, a two-step process is considered to investigate the effects of measurement errors on EWMA and cause-selecting EWMA control charts. At the end of current process, a pair of imprecise measurements of in-coming quality and out-going quality is randomly taken with individual units. The linear relationship between in-coming quality and out-going quality is assumed and four possible states of the process are defined with respective distributions of in-coming and out-going qualities derived. The EWMA control chart with measurement error is then constructed to monitor small-scale shift in mean for the previous process while the cause-selecting control chart, or EWMA control chart based on residuals, including measurement error, is proposed to diagnose the state of current process. Based on sensitivity analysis, the presence of imprecise measurement diminishes the power of both the EWMA and the proposed control charts and affects the detectability of process disturbances. Further, applications of proposed control charts are demonstrated through a numerical example to show some possible misuses of control charts. If the process mean shifts in a small scale when a single assignable cause occurs on each step, the proposed cause-selecting control chart is more sensitive than other control charts. The Hotelling T^2 control chart is also compared to illustrate the diagnostic advantage outweighed by proposed cause-selecting control chart.
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Lin, Chen-Chun, and 林貞君. "The Design of EWMA Control Charts for Censored Data under Gompertz Lifetime Distribution." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/pdx9kc.
Full textAbstract:
碩士淡江大學
統計學系碩士班
95
Shewhart control charts are popular in practical SPC applications due to the simplicity of operation.Over the past years,long lifetime products are designed and manufactured based on the rapid development of high technology.Consider the limitations of cost and the test time,the manufactures often collect censored data to detect if the mean lifetime of products reaches the required specification.Hence,Shewhart control charts are inadequate to be used in such situations due to the normality assumption is violated.Steiner & Mackay(2000) established a CEV X-bar control chart to monitor the mean lifetime of products with censored data.They conclude that the CEV X-bar control chart performs better than Shewhart X-bar control chart bases on the Type I right censored data.In this thesis,I develop an EWMA CEV control chart based on a new statistic proposed by Zhang (2005) and the one side EWMA CEV control chart established by Zhang & Chen(2004).The proposed control chart can detect the mean lifetime of products increase or decrease,simultaneously.A numerical study is conducted to evaluate the performance of the proposed method.
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Cai, Shin-En, and 蔡仕恩. "Applying Run Rule to EWMA Control Charts for Autoregressive Processes with Artificial Neural Networks." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/yu3j8t.
Full textAbstract:
碩士國立雲林科技大學
工業工程與管理系
103
With technological advancements, SPC has been used in industry to reduce the failure rate. Traditional SPC is good at detecting large changes in process mean but not in small changes. Many studies analyzed EWMA charts, and it was mainly used to detect manufacturing process mean which occurred on small offset change. In past studies, SPC was developed independently and normal distribution. Traditional chats in the autocorrected processes cannot complete the monitoring effectand increase the false alarm rate effectively. In this study, we used the EWMA statistic and Run Rules for Autoregressive model data with artificial neural networks and compared to different exponential for mean shift influent. The ARL was examined to evaluate the performance of the process ability and improve the performance of shift. Keywords: Exponentially Weighted Moving Average Control Chart, Artificial Neural Networks, Autoregressive Model
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LIAO, YI-JIE, and 廖逸捷. "Applying Run Rule to EWMA Control Charts for Autoregressive Processes with Support Vector Machines." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/55756159170402620557.
Full textAbstract:
碩士國立雲林科技大學
工業工程與管理系
104
Due to industrial progress and developments, there are many autocorrelation information generated. Traditional Shewhart control chart draw is simple to understand, and use has been widely discussed. But it can not effectively monitor the autocorrelation of process, likely to cause error in judgment, and this control chart is not good at detecting small changes in process mean. So some author have proposed Exponentially Weighted Moving Average (EWMA) chart to monitor small shifts in the process. Literature indicates, support vector machine (SVM) was applied to recognize shifts in autocorrelated processes. In this study, we used the EWMA chart and run rule for Autoregressive model data combined with support vector machine to establish a detecting system. For faster detection of abnormalities. Finally, the results reveal run rule combine SVM performed better than only use run rule to monitor small shifts.
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Bo-WeiChien and 錢柏維. "A Comparative Study on the Detecting Performance for Various EWMA-type Multivariate Control Charts." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/22533283387800665867.
Full textAbstract:
碩士國立成功大學
統計學系碩博士班
101
When implementing a multivariate control chart, in-control parameters of the process are usually unknown in practice. Thus, we need to estimate in-control parameters using in-control data set from Phase I analysis and replace these parameters with their estimates. Moreover, the detecting performance of multivariate control charts in Phase II is affected by number of samples in Phase I. Several authors have dealt with this problem for process mean, but most previous studies did not compare the effect of parameter estimates on the detecting performance of various EWMA-type multivariate control charts in Phase II for simultaneously monitoring mean and variability, and the test statistics of these EWMA-type multivariate control charts are established when parameters are known. In this paper, we propose a RMax-MEWMA control chart in which the unknown in-control parameters are replaced by their estimates based on Chen’s Max-MEWMA control chart. Then, the detecting performances of our RMax-MEWMA and other EWMA-type multivariate control charts are compared. The simulation results show that RMax-MEWMA chart outperforms the other EWMA-type multivariate control charts in terms of in-control and out-of-control ARLs when number of samples is small.
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Yeh, Pei-Fang, and 葉珮芳. "Statistical Monitoring Procedure for Multiple Readings from Multiple Quality Characteristics with EWMA Control Charts." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/04947853875552593602.
Full textAbstract:
碩士國立交通大學
工業工程與管理系
90
An increasing number of wafer fabrication manufacturers use a control chart to effectively monitor the wafer manufacturing process. However, conventional control charts are designed for detecting a manufacturing process with single source of variation, and, therefore, they are incapable of detecting assignable causes for a process with several sources of variation. Moreover, a statistical monitoring procedure for a complex wafer manufacturing process usually involves multiple readings from multiple quality characteristics with several sources of variation. This study presents a competent on-line control process capable of detecting assignable causes concealed behind multiple characteristics and multiple readings in a manufacturing process with several sources of variation. Principal component analysis (PCA) is employed to form new variables, which are the key components of original multiple characteristics in a manufacturing system. Their formation decreases the number of control charts since PCA reduces the number of related features. The Hotelling T2 and multivariate exponential weight moving average (MEWMA) control charts are then used to determine whether the process is in control. Additionally, for a situation in which Hotelling T2 or MEWMA indicates that the process is out of control, three unique X-bar & EWMA control charts of different sources of variation are developed to identify the source of variation. Simulation results indicate that, in addition to detecting small shifts in a manufacturing system, the proposed procedure can accurately identify which sources of variation or characteristics are out of control. In addition, an example from a silicon epitaxy wafer process employed by a Taiwan IC fabrication manufacturer demonstrates the proposed approach’s effectiveness. Results in this study can provide a valuable reference for engineers when attempting to quickly assess the conditions of a wafer manufacturing process. By effectively responding to this information, engineers can promptly adjust the manufacturing system to enhance wafer quality.
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"Statistical Monitoring and Control of Locally Proactive Routing Protocols in MANETs." Doctoral diss., 2012. http://hdl.handle.net/2286/R.I.14511.
Full textAbstract:
abstract: Mobile ad hoc networks (MANETs) have attracted attention for mission critical applications. This dissertation investigates techniques of statistical monitoring and control for overhead reduction in a proactive MANET routing protocol. Proactive protocols transmit overhead periodically. Instead, we propose that the local conditions of a node should determine this transmission decision. While the goal is to minimize overhead, a balance in the amount of overhead transmitted and the performance achieved is required. Statistical monitoring consists of techniques to determine if a characteristic has shifted away from an in-control state. A basic tool for monitoring is a control chart, a time-oriented representation of the characteristic. When a sample deviates outside control limits, a significant change has occurred and corrective actions are required to return to the in-control state. We investigate the use of statistical monitoring of local conditions in the Optimized Link State Routing (OLSR) protocol. Three versions are developed. In A-OLSR, each node uses a Shewhart chart to monitor betweenness of its two-hop neighbourhood. Betweenness is a social network metric that measures a node's influence; betweenness is larger when a node has more influence. Changes in topology are associated with changes in betweenness. We incorporate additional local node conditions including speed, density, packet arrival rate, and number of flows it forwards in A+-OLSR. Response Surface Methodology (RSM) is used to optimize timer values. As well, the Shewhart chart is replaced by an Exponentially Weighted Moving Average (EWMA) chart, which is more sensitive to small changes in the characteristic. It is known that control charts do not work as well in the presence of correlation. Hence, in A*-OLSR the autocorrelation in the time series is removed and an Auto-Regressive Integrated Moving Average (ARIMA) model found; this removes the dependence on node speed. A*-OLSR also extends monitoring to two characteristics concurrently using multivariate cumulative sum (MCUSUM) charts. The protocols are evaluated in simulation, and compared to OLSR and its variants. The techniques for statistical monitoring and control are general and have great potential to be applied to the adaptive control of many network protocols.Dissertation/Thesis
Ph.D. Computer Science 2012
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Hsiao, Wen-Hua, and 蕭雯華. "Application of Taguchi Loss Function in the Economic Design of the EWMA Control Charts with Weibull Failure Mechanism." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/81280544518540728063.
Full textAbstract:
碩士淡江大學
統計學系
92
Control charts are basic and powerful tools in statistical process control and are effective to improve the quality and productivity. Therefore, many researchers have focused on enhancing the capability of various control charts to detect process shifts. Traditional Shewhart control charts are relatively inefficient in detecting small shifts of the process mean. Alternative control charts, such as the CUSUM control and the EWMA control chart, have been developed to compensate for the inefficiency of Shewhart control charts. In this research, we assume that EWMA control chart is used for supervising the process and an economic design based on Duncan’s(1956) cost model for EWMA control charts with a Weibull process failure mechanism has been developed. The Quadratic loss function which was defined by Taguchi was adopted in formulating the cost model. The minimum cost and the optimal design parameters of economical EWMA control charts are determined by using optimal techniques. Our sensitivity analyses also show process parameters which affect the cost significantly.
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Witarsa, Edwin, and 黃維福. "A Study of Performance Comparison among Shewhart, CUSUM, and EWMA Control Charts in Monitoring a Long-Span Bridge Deformation." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/w355dj.
Full textAbstract:
碩士元智大學
工業工程與管理學系
107
Structural Health Monitoring is one of the most important things that must be considered in establishing a building, one of them is the construction of a bridge. Bridge Health Monitoring (BHM) is a condition that must be met in the construction of a bridge to monitor the real condition of the bridge. Suramadu Bridge is a long-Span bridge located in Indonesia that connects the city of Surabaya and the city of Madura. In order to achieve one of the requirements of BHM, a GPS sensor is one of the sensors is used in Suramadu bridge to provide the condition of the bridge in real-time and three coordinates of the object, Latitude, Longitude, and Height coordinates are generated by GPS sensor are believed to represent the real condition of the Suramadu bridge. But the generated data from this GPS sensor must be processed statistically to provide an accurate warning. This study provides a study of Statistical Process Control (SPC) tools comparison, in order to know the behavior of the SPC tools, which are CUSUM and EWMA in detecting the deformation of the bridge. ARL and the lengths of the error are 2 parameters which will be tested for each chart in assessing the speed of the chart in detecting any kind of magnitude of shift and seeing errors generated from the chart respectively. Simulated auxiliary shifts and the occurrence of the chart towards the following occurrences. Some of the results can be found from this study such as CUSUM and EWMA charts have proven as an improvement chart from Shewhart's basic chart in terms of detecting the smaller shift. The results of the comparison between CUSUM and EWMA for this case are CUSUM chart outperforms the EWMA chart in terms of error lengths, it is found that from the error length of EWMA is the lower for all occurrences. In the end, this chart provides the result of the performance of the chart in detecting the simulated additional shift.
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Chang, Che-Yuan, and 張哲源. "The comparison of the monitoring abilities of Shewhart and EWMA control charts when the data come from the MA(1) model." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/78160480881296253960.
Full textAbstract:
碩士國立臺灣科技大學
工業管理系
93
The data are usually supposed to be a random sample from normal distribution in the discussion of control chart. But in actual production process, the machine could create the correlative products because of the attrition. If we still use the traditional control chart to analyze them, it will lead to the wrong conclusion. We assume that the correlative data come from the time series model, MA(1), to discuss the monitoring abilities of EWMA control chart and Shewhart control chart. Since it is not true that the white noise is always from the normal distribution in MA(1) model, we also consider other distributions for the white noise in our paper. In theory, it is hard to calculate the ARL values for Shewhart and EWMA control charts, so we compute them by simulation. Finally, the simulation shows that the monitoring ability of EWMA control chart is better than Shewhart control chart for the correlative data.
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Chen, Yung-Chuan, and 陳勇全. "The Performance Comparisons among the Shewhart, EWMA and CUSUM Control Charts for Monitoring the Semiconductor Process: A case study of Photolithography Critical Dimension." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/11399485233552749593.
Full textAbstract:
碩士輔仁大學
應用統計學研究所
99
With the technology and process progressing, the Critical Dimension of DRAM is getting smaller and smaller. According to this reason, the precision of detect ability is requested higher and higher relatively. To the Semi-conductor industry, the Statistic Process Control chart was conducted for years, especially was the Photolithography which used the Shewhart control chart to monitor the Critical Dimension of litho process. The difficulty of Photolithography process is the light-sensitive deviation between the inter-batches of photo-resist which caused the shift of Critical Dimension. Meantime, the insufficient sensitivity of Shewhart control chart was revealed. This paper will appraise the deviation sensitivity of the EWMA, CUSUM and Shewhart control charts by the actual data from the semiconductor company. The result was shown in the article clearly that EWMA and CUSUM control charts could monitor the smaller shifts and more efficient detective ability than Shewhart control chart. The deviation in inter-batches photo-resist detective capacity will be enhanced efficiently, if alternative control chart could be adopted in Photolithography process.
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Ho, Hui-Ching, and 何惠卿. "Combined Shewhart-EWMA Control chart design." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/99475413884235693897.
Full text
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Hsu, Chien Hao, and 徐健皓. "Computerization of the EWMA Median Control Chart." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/70058106251715612352.
Full textAbstract:
碩士聖約翰科技大學
自動化及機電整合研究所
95
The aim of this research is to develop a practical package for the computerization of the Exponentially Weighted Moving Average (EWMA) median control chart. Combining with a quality cost model, this package is easy-to-use and under Chinese interface. With and without the consideration of outliers, several quality cost parameters can be set into this package for the evaluation of the shift-detecting performance and the relative average quality cost. This package can be installed in PC environment. Some research data and an illustrative operational manual are also built-in. The user can learn and execute it easily. With the aid of computer, the relative calculation time is greatly shortened, and the computational errors are prevented. This application software of the EWMA median control chart will be useful for the researchers and the practitioners.
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CHEN, KUAN-HAO, and 陳冠豪. "EWMA control chart by skew normal distribution." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/4budma.
Full textAbstract:
碩士國立臺北大學
統計學系
105
In the quantity control, traditional X Shewhart control chart is usually u sed to detecting large shift,EWMA control chart has a better detection capability for the small shift of offset.Borror (1999) proposed the EWMA design under normal assumptions, pointed out that when the population is Gamma or student t distribution, whether status is in the in-control or out-of-control,there is no difference to status the performance of ARL.But when the population is Skew-Normal distribution,the ARL does not have a similar performance.This paper will discuss the skew normal under the EWMA, and try to promote the traditional EWMA control chart.
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CHEN, YU-LUEN, and 陳顗倫. "Robustness Examination of Phase I EWMA Control Chart." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/53831954812533786672.
Full textAbstract:
碩士高苑科技大學
經營管理研究所
104
The study investigated the performances of Phase I (retrospective) exponential weighted moving average (EWMA) control chart based on various standard deviation estimators, variable and fixed samples of size, and three scenarios which are in-control, shifting starting value and outlier in the normal distribution. The study used Monte Carlo simulation to calculate the probability that an out-of-control signal falls outside control limits of EWMA control charts in the three scenarios. When the population parameters are unknown, the detection abilities of the EWMA are poor. Particularly, the smaller the weight is, the poorer the detection performance is. The study also shows that EWMA using the interquartile range have a good ability of detecting an outlier.
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Shih, Sheng-Lin, and 施勝霖. "A Study of Inventory Management by Applying EWMA Control Chart and Trend Control Chart." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/n6vd77.
Full textAbstract:
碩士國立雲林科技大學
工業工程與管理系
106
ABSTRACT A superior inventory management can maintain enterprises’ competitive and reduce expenditure. The mismanagement of inventory may cause enterprises cash flow, high cost and out of stack problems and so on. Therefore, enterprises must to face these problems. In the past, many scholars conducted research and searched for solutions to different problems of inventory management, including the application of Statistical Process Control (SPC) to inventory management system to monitor the variation of the demand in the market with control charts. Adjusting future reorder point and order quantity to prevent to unnecessary inventory costs and reduce the probability of out of stack. In order to achieve effective monitoring of market demand data, many scholars proposed different control charts and control methods applied to inventory management system. In the past, research could divide into control charts to monitor the variation of the demand or control charts to monitor the variation of inventory. Few studies monitor the variation in demand and inventory research in the same time. Therefore, this study will use the past control market demand and inventory performance better control chart：EWMA control chart and trend control chart were monitoring market demand and inventory to adjust the reorder point the next period. Expect to improve inventory management performance. The result shows that when market demand fluctuations are stable, for example, market demand fluctuations obey normal distribution or uniform distribution that the study model is prone to errors and the performance reduced. On the other hand, when market demand fluctuations rise slowly, rise rapidly, decline rapidly or change seasonally that the performance of this research model is better. Keywords：Trend control chart, EWMA control chart, Inventory management
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Shu-Fen, Chang, and 張淑芬. "Using Analysis of Means to Innovate EWMA Control Chart." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/85908476380344771135.
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Chang, Wang-wei, and 張王瑋. "The application of EWMA control chart and GWMA control chart in Taiwan stock exchange market." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/27ffgf.
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碩士國立臺灣科技大學
工業管理系
94
As the time goes by, the past method to accumulate wealth won’t let people accumulate wealth, so we must invest. In Taiwan, the stock is the most popular investment; therefore this research will focus on the Taiwan stock exchange market from 2005 March to 2006 February. This research will use the EWMA and GWMA control charts to monitor the stock price. When the data is out of the control limits, we take it as a trading signal. This research will compare the achievement of two control charts with different parameters when ARL is 500. The GWMA control chart with parameters q=0.8,alpha=0.75,L=3.001 has the highest average total reward rate in all the methods.
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余翊寧. "Design of EWMA control charats for controlling dependent process steps." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/33332176308239161733.
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碩士國立政治大學
統計研究所
96
Control charts are used to effectively monitor and determine whether a process is in-control or out-of-control. The properties of EWMA control charts on a single process have been discussed by many researchers. They have proved that EWMA control charts detect small shifts in means or variances more quickly than the traditional Shewhart control charts. However, many products are currently produced in several dependent process steps. In this article, (1) we propose three kinds of EWMA control charts, - , - , and a combined control charts, to monitor the process mean and variance for a single process step, and (2) extend the three kinds of EWMA control charts in (1) to control two dependent steps. The performance of the proposed control charts is measured by using the Markov chain approach. The application of the proposed control charts is illustrated by using some numerical examples, and the performance of the proposed charts is compared by using some numerical examples. The adjusted average time to signal (AATS) and the adjusted average samples to signal (ANOS) are calculated to measure the performance of the proposed EWMA control charts by Markov chain approach. A data set consisting of the measurements of the inside diameter of the cylinder bores in an engine block example illustrates the applications of the three kinds of EWMA control charts for a single step and a empirical automobile braking system example illustrates the applications of the three kinds of EWMA control charts for two dependent steps. Moreover, their performances are compared by some numerical analysis results.
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Wei, Lu-Chen, and 韋祿甄. "Economic Design of the Variable Sampling Intervals EWMA control chart." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/13426603749788408187.
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碩士國立雲林科技大學
工業工程與管理研究所碩士班
92
Control charting is a graphical expression for monitoring process. In this paper, we develop the economic design of the variable sampling intervals EWMA control chart determined by the parameter set(the sample size, the control limit, the warming limit, the short sampling interval, the long sampling interval and the weight constant) that minimizes the total cost. Use the genetic algorithm to search for the optimal parameter set and illustrate the solution procedure by an example. Sensitivity analysis is then carried out to investigate the effects of model parameters on the solution of the economic design.
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Kang, Fu-Sen, and 康富森. "The Research on Non-normality of individual EWMA Control Chart." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/20742848115769457358.
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碩士淡江大學
統計學系
89
We usually suppose the quality characteristic is normal distribution on variables control chart. Nelson(1976) discuss x-bar control chart on non-normality.Their study indicates that datas for small value of r in gamma distribution (r=0.5 and 1) will cause largeα-risk’s difference with normality. In this paper, I will discuss individual EWMA control chart on non-normality. I will use charting, Gaussian quadrature and simulations to discuss the dependence on normality of the two control chart. In this paper, we can discover that x control chart is more sensitive departures from normality than individual EWMA control chart.
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Jhen-YuWang and 王振昱. "Developing Multivariate EWMA Control Chart for Skew Normal Profiles Data." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/q6h9g2.
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Wu, Po-Hsu, and 吳柏栩. "Double Moving Average – EWMA Control Chart for the Weibull products." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/fh2435.
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碩士國立嘉義大學
應用數學系研究所
106
The control chart was first proposed by Shewhart in 1924. The tools used to monitor product processes, although simple to use and effective, have the disadvantage of being insensitive to slight deviations in the process average or variance. Exponentially weighted moving average (EWMA) control chart was proposed by Robert in 1959, which is suitable for detecting slight deviations in the average value of processes. In this paper, we assume that the process sample data of the product are subject to Weibull distribution with shape parameters and scale parameters , and a new double moving average – exponential weighted moving average(DMA-EWMA) control chart is formed by combining the double moving average and exponential weighted moving average control chart. In this paper, we use the least square method to obtain the estimated values of the shape parameters and scale parameters for the process sample data of the product, and then use the DMA-EWMA control chart proposed in this paper to evaluate whether the product meets the industry’s demand standards, and verify that this control chart is feasible.
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Yang, Yung-Yu, and 楊永瑜. "A Research and Application of Negative Binomial EWMA Control Chart." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/48718860091607635217.
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碩士大葉大學
工業工程與科技管理學系
98
The traditional control chart for nonconformities (called C control chart) assumes that process nonconformities have Poisson distribution. In actuality however, the occurrence of nonconformities does not always observe Poisson distribution. For example, when nonconformities of wafers have clustering phenomenon in semiconductor production process, the process control based on Poisson distribution always underestimates the true average nonconformities and process variance. If the compound Poisson process is taken as the basis for process control, the quality feature could be described accurately. When the process has minor variation, the sensitivity of the exponentially weighted moving average (EWMA) control chart is higher than the C control chart and more accurately reflects the current situation of the process on the control chart. Hence, this study considers Poisson-Gamma compound distribution for the failure mechanism, and takes the Markov chain approach to calculate the average run length required by the EWMA control chart under different design parameters. Moreover, the EWMA control chart of Poisson-Gamma compound distribution was constructed and actual data from a wafer plant were employed to illustrate the model’s working. This study could be used for detecting minor process variations in wafer plants and improving the process quality.
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鄭舜壕. "A Nonparametric EWMA-Type Signed-Rank Control Chart with Time-Varying Control Limits." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/17844227873799593243.
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碩士國立政治大學
統計研究所
98
According to Steiner (1999), the control limits of exponentially weighted moving average (EWMA) control charts should vary with time, so that the charts would have properties similar to the fast initial response (FIR) feature, when compared with asymptotic X-bar EWMA charts. However, previous analyses of nonparametric EWMA control charts consider only asymptotic control limits and are not sensitive to the shifts in a process at early stages. In this thesis, a nonparametric control chart with time-varying control limits is constructed based on EWMA control chart built upon the Wilcoxon signed-rank statistics. When the underlying distribution is normal, uniform, or double exponential, the average run lengths in both in-control and out-of-control conditions are approximated using non-homogenous Markov chain and based on Monte Carlo simulations. Simulation results show that EWMA charts with varying control limits are more efficient to detect early process shifts when weighting constants are small, and the underlying distributions are heavy-tailed distribution (such as double exponential distribution).
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Jhuang, Jhen-Ning, and 莊鎮寧. "The Study on the Application of Self-Weighting EWMA Control Chart." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/73417063611395786205.
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碩士正修科技大學
工業工程與管理研究所
99
The development of hi-tech industry increased with the progress level of technology and economic in these days that enlarge the focus weightiness on the product quality of consumers. The problem of that how to monitor the product quality in manufacturing process plays an important role when the uncertainties during the manufacturing process appear that affects the product quality. To monitor the process quality, the control charts used for the tool in the manufacturing process generally, that can’t support the early warning of future changes such as tracking trends which can induces the cost loss. According to the manufacturing process of 3C products, the variation of temperature is one of the important factors that influence the motherboard quality. In order to enhance monitoring power in manufacturing, a new modified EWMA control chart was established and verified as the tool in the manufacturing process that revises the Weight mode of original EWMA control chart. Results show that self-Weighting EWMA control chart can detect the uncertainty early and respond the process state that is fine for manufacturing process monitoring when the process adopts the real time process control. Furthermore, the study uses measurement techniques with computerized monitoring system integrated Microsoft Office software “EXCEL”, it offers a good reference to the relevant industry for improving product quality and efficiency of process control.
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Huang, Chih Chuen, and 黃志全. "Economic Design of EWMA Control Chart Using the Taguchi Loss Function." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/65914041110768689276.
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碩士國立雲林科技大學
工業工程與管理研究所
87
This research is based on Duncan’s (1956) cost model to develop an economic design of EWMA control chart. Taguchi’s loss function which is more suitable for today’s quality cost definition was adopted in formulating the cost model. In order to investigate the effects of loss function on the economic design of control chart, a comparison between EWMA control chart and X-bar control chart was conducted. A sensitive analysis was also performed for the model developed. Result show that the optimal value of weighting constant (r) is not necessarily small from an economic viewpoint. Parameters related to loss function such as A , P , and △ have major influence on the expected cost function.
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Liang, Long-Sheng, and 梁隆盛. "A Study of the Development of Combined EWMA-CUSUM Control Chart." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/95948158796952782127.
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碩士國立雲林科技大學
工業工程與管理研究所碩士班
89
Since the development of Shewhart control chart, they have been used as the major statistical quality control tool. However, for detecting small process shifts, the Shewhart control chart is not as sensitive as the CUSUM or EWMA charts. To improve the sensitivity of CUSUM and EWMA charts in detecting large shifts, the combined Shewhart-CUSUM and Shewhart-EWMA charts were developed. In this research, we try to develop a new control chart called EWMA-CUSUM chart, which sustains the advantage of the EWMA and the CUSUM chart. Meanwhile, the investigation of Average Run Length property for different shift sizes will also performed. A comparison of EWMA-CUSUM chart with other charts that include EWMA, CUSUM and Shewhart control chart will be conducted. The source data are transformed to the EWMA statistics in the EWMA-CUSUM chart. By simulation, we first obtain values of parameter h’ used in the EWMA- CUSUM chart when ARL0 is equal to 370. Then, under different shift sizes, the ARL1 values of EWMA, CUSUM, Shewhart and EWMA-CUSUM charts are compared. In this research has the following conclusions： 1、When λ’ increases, the parameter h’ decreases meanwhile when λ’ increases, the parameter h’ is also decreases under ARL0 is equal to 370. 2、Whenλ’ decreases, the ability of EWMA-CUSUM chart in detecting small shifts (δ=0.05∼0.25) is not only better than EWMA chart but also better than CUSUM chart. The ability of EWMA-CUSUM chart in detecting larger shifts is the worst of all the charts whenλ’ increases. 3、For larger shifts (δ=3∼4), the Shewhart chart still has the shorter ARL than others. When shifts are small than 0.25, the EWMA-CUSUM chart is the best of all charts compared.
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黃立芬. "An EWMA Control Chart for Detecting Increases in Multivariate Process Variation." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/31281057257720585070.
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Jheng, Yi-Wun, and 鄭怡文. "Applying Risk-Adjusted EWMA Control Chart in Monitoring Lung Cancer Surgery." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/92969322946014445940.
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碩士國立雲林科技大學
工業工程與管理系
103
ABSTRACT Exponential Weighted Moving Average Control Chart (EWMA) is a useful technique of statistical process control in industrial. It has been applied to medical outcomes for monitoring the success rate of surgery, public-health surveillance, infection control, etc. Different from homogeneity of industrial product, each patient is heterogeneous. Therefore, it is necessary to adjust the risk for each patient before performing the control chart. In this study, two risk adjusted methods of logistic regression model and cox model are adopted. The EWMA control chart is used to monitor the medical outcomes. The performances of two risk adjusted methods will be compared. The Lung cancer surgery data will be applied for demonstration. The results show that the cox model is more quickly to detect the abnormality than logistic model. Keywords : EWMA control chart, Logistic regression model, Cox model
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HSU, YUAN-HAN, and 許元瀚. "Applying Singular Spectrum Analysis and Support Vector Machine in Concurrent Control Chart Patterns Recognition on EWMA Control chart." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/ub9u7u.
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碩士國立雲林科技大學
工業工程與管理系
106
In industrial manufacturing, Statistical Process Control (SPC) has become widely used in manufacturing processes, and control charts are the most commonly tools for quality management. When the process is abnormal, the control chart will appear abnormal pattern. Correctly identify the abnormal pattern can know the cause of abnormal in process. In most patterns recognition research, that assumed only single abnormal pattern, actually, multiple mixed pattern will occur in the process. Most of the past research have used ICA to separate mixed data, independent component analysis is more suitable for multivariable process .But, there has the defect of inherent permutation and scaling ambiguities. Therefore, this study proposed singular spectrum analysis and support vector machine identification EWMA control chart of the mixed patterns, the singular spectrum analysis is a time series analysis, that is used on separate mixed data to single abnormal pattern, and then input support vector machine to determine the patterns. The research’s result found when λ reduce on EWMA control chart , the current recognition performance would be much better than others, and training sample size has more bigger, the result would be more better.