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1
Sepúlveda, Ariel. "The Minimax control chart for multivariate quality control." Diss., Virginia Tech, 1996. http://hdl.handle.net/10919/30230.
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Böhm, Walter, and Peter Hackl. "CUSUM Chart for Correlated Control Variables." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1991. http://epub.wu.ac.at/76/1/document.pdf.
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The cumulative sum (CUSUM) technique is well-established in theory and practice of process control. A comprehensive exposition of the method is given, e.g., by Wetherill and Brown (1991). A question that is seldom treated in the literature is that on the effect of serial correlation of the control variable. Johnson and Bagshaw (1974) investigate the effect of correlation on the run length distribution when the control variable follows a first order autoregressive or moving average process. They also give an approximate expression for the average run length of the CUSUM- technique for correlated control variables. In this paper we derive an exact expression for the average run length of a discretized CUSUM-technique, i.e., a technique that uses a scoring system for the observations of the control variable. The scoring system is that suggested by Munford (1980). Our results are derived for a control variable that is assumed to follow a first order autoregressive process and with normally distributed disturbances. After deriving in Section 2 the expression for the average run length we discuss its dependence on the process parameter and give a numerical illustration. In Section 3 we discuss corrections for the CUSUM-technique in order to keep the nominal risk for an out-of-control decision and compare our results with those given by Johnson and Bagshaw (1974). (author's abstract)Series: Forschungsberichte / Institut für Statistik
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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.
4
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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Tian, Wen Jing. "A multivariate control chart for monitoring univariate processes." Thesis, University of Macau, 2006. http://umaclib3.umac.mo/record=b1675975.
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Chung, Jain. "Control chart procedures based on cumulative gauging scores." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/54277.
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Control charts based on cumulative gauging scores rely on gauge scoring systems used for transforming actual observations into integer gauging scores. In some cases, the gauging scores are easy to obtain by using a mechanical device such as in the go-no-go inspection process. Thus, accurate measurements of selected quality characteristics are not necessary. Also, different control purposes can be achieved p by using different scoring systems.
Cumulative gauging score charts based on two pairs of gauges are proposed to control the process mean or the standard deviation by either gauging one or several observations. Both random walk and cusum type cumulative gauging score charts are used. For controlling the process mean and standard deviation at the same time, a cusum type and a two-dimensional random walk type procedure are proposed. A gauging scheme can be applied to multivariate quality control by gauging either x² or T² statistics. A simple multivariate control chart which is based on the multivariate sign score vector is also proposed.
The exact run length distribution of these cumulative gauging score charts can be obtained by formulating the procedures as Markov chain processes. For some procedures, the average run length (ARL) can be obtained in a closed form expression by solving a system of difference equations with appropriate boundary conditions.
Comparisons based on the ARL show that the cumulative gauging score charts can detect small shifts in the quality characteristic more quickly than the Shewhart type X-chart. The efficiency of the cusum type gauging score chart is close to the regular CUSUM chart. The random walk type gauging score chart is more robust than the Shewhart and CUSUM charts to observations which have heavy a tailed distribution or which are serially correlated. For multivariate quality control. A procedure based on gauging the x² statistic has better performance than the x² chart. Also, a new multivariate control chart procedure which is more robust to the misspecification of the correlation than the x² chart is proposed.Ph. D.
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Myslicki, Stefan Leopold 1953. "A VARIABLE SAMPLING FREQUENCY CUMULATIVE SUM CONTROL CHART SCHEME." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276503.
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This study uses Monte Carlo simulation to examine the performance of a variable frequency sampling cumulative sum control chart scheme for controlling the mean of a normal process. The study compares the performance of the method with that of a standard fixed interval sampling cumulative sum control chart scheme. The results indicate that the variable frequency sampling cumulative sum control chart scheme is superior to the standard cumulative sum control chart scheme in detecting a small to moderate shift in the process mean.
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Silverlycke, Peter. "Vidareutveckling av grafkomponent." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-23318.
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Rapporten täcker vidareutvecklingen av en grafkomponent som från början kunde visa linjediagram med datapunkter bestående av reella tal. Grafkomponenten var en del av programvaran Tunnel Manager som är utvecklad av Sogeti till Atlas Copco. Tunnel Manager används i kombination med Atlas Copcos borrigg Boomer. Grafkomponenten utvidgades med stapeldiagram med flera serier, stapeldiagram med adderade serier och med cirkeldiagram. Den utvidgades även med nya datapunktstyper i form av datum och tid. Även gruppering av data för stapeldiagram lades till. Utökad information visades också när muspekaren hölls över ett diagram, ett så kallat tooltip. Zoom och panorering i diagrammen implementerades så användaren kunde granska vissa områden i detalj. Rapporten omfattar även en utredning där det undersöktes vilken information och vilka diagram Atlas Copco hade behov av i framtiden i Tunnel Manager. Det visades sig att det fanns stort behov av att visa diverse information i diagram för att få ett bra underlag till beslutsfattning. Dels för planering av användandet av borriggen. Dels för underhåll av borriggen. När stora mängder information samlas in behövs bra sätt att sammanfatta den på. Diagram är ett mycket bra sätt för detta ändamål. Diagrammen behöver dock följa vissa grundläggande regler för att de ska vara tillförlitliga. Bland annat att diagram som jämförs ska ha samma skala för att underlätta jämförelsen. Vidareutvecklingen av grafkomponenten tog hänsyn till dessa regler, det bidrog till att den lämpar sig att använda i produktion.This report covers the further development of a chart component. The component could display a linechart with real number datapoints at the beginning. The chart component was part of as software called Tunnel Manager, developed by Sogeti for Atlas Copco. Tunnel Manager is used in combination with Atlas Copcos drilling rig Boomer. The charts added were barchart with support for several dataseries, stacked barchart with support for stacked dataseries and piechart. A new datapoint type for date and time was added. Grouping of data for the barcharts was also added. Extended information was shown when the mouse pointer was held over a diagram, a tooltip. Zoom and panning in the charts was implemented, allowing the user to view some parts in detail. The report also covers an investigation. The investigation finds out what kind of information, and what kinds of charts Atlas Copco had need of in the future in Tunnel Manager. There was a great need for displaying information in charts to get a good base for decision making. The information was needed for planning and maintenance of the drilling rigs. When a lot of information is gathered from different sources a good way is needed for compilation and displaying of the information. Charts are a very good way of doing this. The carts need to follow a set of basic rules to be trustworthy. For example if several charts is to be compared, they need to have the same scale, to make it easier to compare. The further development of the chart component took these rules into account and it made it suitable for usage in production.
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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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De, La Torre Gutiérrez Héctor. "A modelling-oriented scheme for control chart pattern recognition." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7666/.
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Control charts are graphical tools that monitor and assess the performance of production processes, revealing abnormal (deterministic) disturbances when there is a fault. Simple patterns belonging to one of six types can be observed when a fault is occurring, and a Normal pattern when the process is performing under its intended conditions. Machine Learning algorithms have been implemented in this research to enable automatic identification of simple patterns. Two pattern generation schemes (PGS) for synthesising patterns are proposed in this work. These PGSs ensure generality, randomness, and comparability, as well as allowing the further categorisation of the studied patterns. One of these PGSs was developed for processes that fulfil the NIID (Normally, identically and independently distributed) condition, and the other for three first-order lagged time series models. This last PGS was used as base to generate patterns of feedback-controlled processes. Using the three aforementioned processes, control chart pattern recognition (CCPR) systems for these process types were proposed and studied. Furthermore, taking the recognition accuracy as a performance measure, the arrangement of input factors that achieved the highest accuracies for each of the CCPR systems was determined. Furthermore, a CCPR system for feedback-controlled processes was developed.
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Moraes, Denis Altieri de Oliveira. "A self-oriented control chart for multivariate process location." Universidade Federal de Minas Gerais, 2014. http://hdl.handle.net/1843/BUBD-9H6H8V.
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In this work we present comparative studies as well as new proposals on methods for statistical process control. Specifically, multivariate control charts with emphasis on monitoring the mean vector of Gaussian processes with individual observations. The statistical process control where only one observation is available at each instant of time is a difficult problem to approach, since it is not possible to accurately estimate the current process centre by means of Shewhart-type control charts, in which case it is essential to utilise non-Shewhart control charts, i.e., to consider at the current instant also information from past observations. Regard to this, several experiments were initially carried out in order to verify the robustness of the traditional methods based on the non-centrality parameter. Next, we investigated alternatives to the most common method used in practical applications, the MEWMA scheme, such as sliding window schemes for estimation of the current mean vector of the process. Finally, new control charts have been proposed, also based on the non-centrality parameter, but utilising a different criterion to obtain a linear transformation, more efficient than the known method Principal Component Analysis. It was found through experiments that the proposed statistics fills a gap regarding to the application of automata schemes for monitoring the centre of multivariate processes, being more efficient in terms of speed detection of shifts than the traditional quadratic approaches for a wide range of distances.Nessa tese são apresentadas análises comparativas de cartas de controle tradicionais e também novos métodos para o monitoramento dos vetores de médias em processos multivariados. O trabalho aborda os gráficos de controle multivariados para o monitoramento do vetor de médias de processos gaussianos com observações individuais. O controle estatístico do processo em que apenas uma observação está disponível a cada instante do tempo é um problema de difícil abordagem, já que não é possível detectar precisamente o deslocamento do vetor de médias por cartas do tipo Shewhart. Nesse caso é imprescindível o uso de cartas do tipo não-Shewhart, ou seja, considerar no instante atual a informação proveniente de observações passadas. Nesse sentido, diversos experimentos foram inicialmente realizados com o propósito de verificar a robustez dos métodos tradicionais baseados no parâmetro de não-centralidade. Foram investigadas alternativas ao método mais utilizado em aplicações práticas, o método MEWMA, com o uso de janelas deslizantes para a detecção de mudanças no vetor de médias do processo. Finalmente, foram propostos nesta tese novos gráficos de controle, também baseados no parâmetro de não-centralidade, contudo utilizando uma transformação linear mais eficiente que o método Análise de Componentes Principais. Verificou-se através de simulações de Monte Carlo que a estatística de controle proposta preenche uma lacuna existente quanto à aplicação dos métodos automáticos para o controle do vetor de médias de processos multivariados, sendo mais eficiente em termos de rapidez de detecção das mudanças do que os gráficos tradicionais em diversas situações.
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Hui, Huang Ken, and 黃耿輝. "Economic-based Multivariate Control Charts--T sqare Control Chart." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/83367692541454652700.
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吳采玲. "Combined Multivariate Control Chart." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/20944561960609603202.
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碩士國立交通大學
統計學研究所
96
A new combined control chart for multivariate distribution is proposed. This control chart may be applied on any distribution that its joint probability density function in terms of a random sample is known its distribution while the existed multivariate control charts are generally designed only for multivariate normal distribution. A comparison of this chart with a moving average control chart by Chen, Cheng and Xie (2005) for bivariate normal distribution shows that it is very competitive. When the joint probability density function is not known in its distribution, an approximate combined chart is proposed. Studies of ARLs for these two charts are performed and displayed.
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Yang, Chih-Wen Ou, and 歐陽智聞. "Functional data control chart." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/94610249516502752261.
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博士國立臺灣科技大學
工業管理系
100
In manufacturing process, a sequence of measurements of quality characteristic is increasingly taken across some continuum, producing a curve that represents the quality of the item. This curve provides the so-called functional data. Traditionally, functional data is treated as a special type of profile data. Regardless of a linear or nonlinear profile, the common approaches of the control chart are based on the multivariate control chart by monitoring the estimated parameter of the predefined linear or nonlinear model. Usually, the model is difficult to know practically, and it is also difficult to identify the abnormal pattern from the outlying parameter. By using the techniques of functional data analysis, we proposed the functional data control chart which can provide a better solution to these problems. In the Monte Carlo simulations, we show that the functional data control chart is sensitive when the underlying process status is changed. By applying to real example data, the new method exhibits a good performance.
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Yu, Jeng-Hung, and 于政宏. "Multivariate JS Control Chart." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/84609835890902867951.
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碩士國立交通大學
統計學研究所
98
In this study, we focus on improving Phase I study to construct more accurate Phase II control limits for multivariate variables. For a multivariate normal distribution with unknown mean, the usual mean estimator is known to be inadmissible under the squared error loss when the dimension of variables is greater than 2. Shrinkage estimators, such as the James-Stein etc., are shown to have better performance than the conventional estimator in the literature. When considering a low defect or high yield process, we utilize the James-Stein estimator to improve the Phase I parameter estimation. Multivariate control limits based on the improved estimator are proposed in this study. The adjusted control limits are shown to have substantial improvements than the existing control limits.
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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.
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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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Huang, Yuan-Jun, and 黃元君. "Attribute control chart information system." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/24173084742692342270.
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Lin, Hung —. Chia, and 林宏嘉. "Revised X-bar Control Chart." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/p6dv4s.
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碩士淡江大學
統計學系
91
This paper presents two approaches for constructing control limits of X-bar control chart that can enable the user to begin monitoring the process mean at an earlier stage than the standard approaches. The proposed control limits can be constructed easily and are closed to any specific percentile of run length distribution of the true limits, even when only a few initial subgroups are available. Performances of the proposed approaches are studied by Monte Carlo simulation. The simulation results show that the proposed control limits perform similarly to the true limits even when the limits are estimated using data from only a few initial subgroups.
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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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Tang, Ming-Yu, and 唐民聿. "Constructing an Autocorrelated Process Control Chart using Modified Grey Model and ACUSUM-C Control Chart." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/61556830921793447553.
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碩士國立交通大學
工業工程與管理學系
100
In today's competitive market, enhancing product quality becomes an important issue for manufacturers. Statistical Process Control (SPC) has been widely employed in many industries. In order to control the process, the control chart is utilized to detect the assignable cause of process shift effectively. However, if the process has strong autocorrelation, the performance of control charts will be affected and false alarms will be increased. In this study, modified grey model and ACUSUM-C control chart are utilized to construct an effective modified procedure to eliminate the impact of autocorrelation in autocorrelated process. Furthermore, both of the small and large mean shifts can simultaneously be detected using the proposed procedure. Finally, a real case and a simulated case are utilized to demonstrate the effectiveness of the proposed procedure.
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Yang, Ching-Hwi, and 楊清暉. "A Study on the Design of Acceptance Control Chart and Reject Control Chart under Non-normality." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/12981605812998730153.
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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
90
Since 1924 when Dr. Shewhart presented the first control chart, statistical methods provide a useful application in industrial process control. The “acceptance control chart” theory conducted by Dr. Freund on 1957. This theory combined the specification limit with both the producer’s risk (type I error) and the customer’s risk (type II error) to find the control limit for process. It’s a tooling put the acceptance sampling together with the control chart. The reject control chart is generally applied in situations when a chart is used to control the fraction of non-conforming units produced by process and where 6-sigma spread of the process is smaller than the spread in the specification limits. Traditionally, when conducting the design of control charts, one usually assumes the measurements in the sample are normally distributed and independent. However, this assumption may not be tenable. If the measurements are asymmetrically distributed and correlated, the statistic will be approximately normally distributed only when the sample size n is sufficiently large and may reduce the ability that a control chart detects the assignable causes. In this paper, we use the Burr distribution, which can be employed to present various non-normal distributions, to determine the appropriate control limits or sample size for the acceptance control chart and reject control chart under non-normality. Some numerical examples are given for illustration. From the presented example，it is noted that ignoring the effect of non-normality in the data will lead to a higher typeΙor type Ⅱ error probability. Keywords：acceptance control chart 、reject control chart 、the Burr distribution
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Ho, Chi Kit, and 何志傑. "Adaptive Sampling Interval Economic Control Chart." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/23552657357685860610.
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溫為閔. "Construction of Control Chart for Variable." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/55607122851723028886.
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碩士明志技術學院
工程管理研究所
92
Statistical process control（SPC） is the method that monitor process quality characteristic. Through control charts, one can detect whether the present process malfunction. However, as one use it, one need choose different control charts in order to match different quality characteristic. But nowadays, any control chart can''t effectively monitor quality characteristic under variable circumstances. According to the aforementioned, that a quality engineer need choose different control charts under different processes will bring annoyance in using it. So, it is necessary to develop a control chart which has good detective effect to different process quality characteristic. Our research uses wavelet transformation drawing wavelet control chart under single quality characteristic. Time series models whose process observation is independent to another, which has first-order autoregressive processes（AR1）, use average run length（ARL） to compare with Shewhart control chart, EWMA control chart, SCC ,and EWMA Control Chart of residual. After simulation, we find that, when process observation is independent to another, the efficiency of wavelet control chart detective abnormality is better than EWMA control chart, and the more the mean shift is, the better the detective efficiency is. When the process is AR(1) time series model and its autoregressive parameter is less than 0.5, wavelet control chart still has fine detective efficiency. When is more than 0.5, the detecting efficiency of traditional SCC is better than wavelet control chart. According to the result of instances, we find that wavelet control chart indeed has better detective efficiency in our research.
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Lim, Kai-Wen, and 林凱文. "Control chart for Bivariate Binomial Data." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/52299341640892997517.
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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
94
To monitor the multi-attribute Binomial data is of interest in many industrial ap-plications and normal approximation is a common tool used to construct control limits when processes are monitored. But normal approximations may not always perform well as expecting, especially when the fraction nonconforming is too small and the sample size is not large enough to approximate normality. Hence, a Bivariate Bino-mial control chart (BB chart) is proposed to deal with the process without normal as-sumption. Simulation study will be conducted to compare the ARL performance with the Bivariate Binomial chart, three-sigma chart and the MNP chart. The results indi-cate that the BB chart not only has the best in-control ARL but also provides a fine ca-pability in detecting process shifts. It is appropriate for quality control in industrial.
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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.
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Chen, Yi-Liang, and 陳奕良. "Control Chart Applied for Encephalitis Data." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/wd93mh.
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碩士國立交通大學
統計學研究所
105
With the rapid development of technology, it is more convenient to store data, and consequently, the amount of data that is being created and stored keeps growing.A famous biomedical data source, The PubMed, is a free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. In this study, we access the literatures of encephalitis from PubMed and adopt control chart methods to monitor the number of literatures with respect to time frame. We consider the three types of encephalitis, Japanese encephalitis, Herpes simplex encephalitis and Limbic encephalitis in this study. The individual and moving range (MR) control charts, as well as, Hotelling Tsquare control charts, time series analysis and the James-Stein estimator, are adopted to analyze the data. The results show that this proposed method may be a useful approach to analyze the occurrence rate of these diseases.
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Huang, Wan-Ting, and 黃婉婷. "Control Chart as a Coverage Interval." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/3mf54z.
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碩士國立交通大學
統計學研究所
106
Control chart is a common tool of quality control. Control chart can be viewed as a coverage interval, with a certain coverage probability. In this article, we reconstruct control chart from statistical point of view, and categorize them into population-type control chart and sample-type control chart. We study on X − S chart under normal distribution and exponential distribution, and also on individuals control charts under normal distribution. In addition, we construct confidence intervals of control charts and compute their coverage probability. Traditional process control actually tests if the process mean of a control chart is changed. The confidence interval of a control chart expands this hypothesis testing to test if the control chart interval is changed.
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Kao, Shih-Chang, and 高世昌. "The Study of Robust Control Chart." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/r299cx.
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碩士銘傳大學
風險管理與統計資訊研究所
92
Choobineh and Ballard (1987) proposes a new method to setting up control limits of charts and this method based on weighted variance method. When the underlying population is symmetric, the weighted variance method gives the same limits as the Shewhart method. Whereas, when the underlying population is skewed, the limits of weighted variance method are adjusted in accordance with the direction of skewness. The weighted variance method only uses first two order moments. However, information of high order moments is disregard. Hence, we use high order moments and least square method to setting up control limits of charts. The new method is compared by simulation with Shewhart method and weighted variance method. When the underlying population is skewed, the new method performs better than Shewhart or weighted variance methods.
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楊正蘭. "Economic design of X control chart." Thesis, 1987. http://ndltd.ncl.edu.tw/handle/26112166764231690780.
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Hsiang, Weng I., and 翁逸翔. "Computerization of the Synthesized Control Chart." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/54380273270147538660.
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碩士聖約翰科技大學
工業工程與管理系碩士班
97
When a process is outliers-existing, using the mean control chart for monitoring the process is too sensitive and always leads to high false-alarms. The median control chart is outliers-resistant and will be a steady control framework. However, while monitoring the small shift of the process, the mean control chart outperforms the median control chart. Recently, there are several good synthesized control charts have been proposed for the outliers-existing process. The purpose of this study is to develop easy-to-use and Chinese interface applications software for the synthesized control charts, such as the Shewhart-based synthesized control scheme and the exponentially weighted moving average-based synthesized control scheme. The Visual Basic is used. Under different process mea n shifts and different degree of the contaminated data, the performance of the synthesized control chart is evaluated via simulation under some certain quality cost parameters setting. Applying the developed software could reduce the human errors and save the computing time.
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Lin, Sung-nung, and 林松農. "Economic Design of ARMA Control Chart." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/70702354159497144746.
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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
95
Control charting is a graphical expression for monitoring manufacture process to detecting quality defect. In a continuous manufacture process, quality characteristic have autocorrelation. Traditional Shewhart control chart isn’t applicable for monitoring manufacture process with autocorrelation. In this paper, we develop the economic design of the autoregressive moving average (ARMA) control chart determined by the parameter set (the sample size, the sampling interval, the control limit, the autoregressive parameter of ARMA control chart and the moving average parameter of ARMA control chart) that minimizes the total control chart cost. Use the simulation technique and 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 parameter of ARMA control chart. Finally, the cost of ARMA chart economic model in this study is effected by the process mean shift level, time length between two assignable causes, expect time for searching assignable causes, expect time for repairing process, total cost per hour of defect products when process is in control and total cost per hour of defect products when process is out of control.
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Lin, Tse-Chieh, and 林澤杰. "Generally weighted moving average control chart." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/37305284766449502393.
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博士國立臺灣科技大學
工業管理系
91
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 ability of the control chart to detect process shifts. In many applications in manufacturing industries, Shewhart control charts are used for statistical process control because they are simple to plot, easy to interpret, and their control limits are easy to obtain. However, Shewhart control charts are relatively inefficient in detecting small shifts of the process mean. Alternative control charts, such as the CUSUM control chart and the EWMA control chart, have been developed to compensate for the inefficiency of Shewhart control charts. The CUSUM and EWMA charts are typically applied to statistical process control in flow production industries. Recently, the promotion of process technology has made quality control stricter. Quality control departments are focusing on how to detect small shifts of the process mean to be able to adjust them in control. This study extends the EWMA control chart to generate what is hereafter called the generally weighted moving average control chart (GWMA control chart). The GWMA control chart, with time-varying control limits to detect start-up shifts more sensitively, performs better in detecting small shifts of the process mean. The implementation and design of the GWMA control chart for monitoring normal random variables and Poisson random variables are discussed. The GWMA control chart uses numerical simulation to determine the average run length of various process mean shifts under variously adjusted parameters. Accordingly, the GWMA control chart is shown to perform better than Shewhart and EWMA control charts in monitoring small shifts of the process mean. Finally, profiles of the average run length are set up with common control limits of the GWMA control chart. Numerical examples are given to illustrate the GWMA control scheme. Keywords: Exponentially weighted moving average control chart; Generally weighted moving average control chart; Average run length
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LAI, WEN-HSIANG, and 賴文祥. "Negative Corrlation Application in Control Chart." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/kp9crh.
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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.
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Tzu-Chien, Chou, and 周姿蒨. "A Method of Improving the Control Chart." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/96366204688053095624.
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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.
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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, Shin-Jia, and 陳信嘉. "Control Chart Patterns Recognition and Parameters Characterization." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/26103225960638071816.
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博士元智大學
工業工程研究所
89
Control chart patterns recognition is an important aspect of statistical process control (SPC). A control chart may indicate an out-of-control condition when some nonrandom patterns occur. Different nonrandom patterns can be associated with a specific set of assignable causes. Hence, identification of nonrandom patterns can greatly narrow the set of possible causes that must be investigated, and thus the diagnostic search could be reduced in length. The purpose of this research is to develop a pattern recognition system to recognize nonrandom patterns and identify the key parameters. In this research we propose two pattern recognizers for the analysis of control chart patterns: a graphical approach and a neural network-based pattern recognizer. The proposed graphical approach is based on the concepts of data smoothing and is capable of detecting unnatural patterns as well as describing the key parameters of the specific pattern detected. The neural network-based pattern recognizer looks for the following nonrandom patterns: trend, cycle, shift and the multiple combinations of these patterns. In addition to the raw data, some important features extracted from process data were used as the inputs of neural network. The pattern recognizers have been evaluated by estimating the average run length, the rate of correct classification and mean absolute percent errors. The simulation results show that the pattern recognizer can recognize the control chart patterns with correct classification rate of about 90%. The results also show that the pattern recognizers developed in this research can accurately identify the key parameters of the patterns detected.
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Theng, Cheng Jane, and 鄧靜楨. "Analysis of Means and Shewhart Control Chart." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/58269616501881732050.
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Leu, Shwu Jiun, and 呂淑君. "The Development of Cause-Selecting Control Chart." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/03799482122546707643.
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Liou, M. L., and 劉美利. "Diagnosis of Control Chart with Fuzzy Logic." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/09092645861635264770.
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碩士國立交通大學
工業工程研究所
83
Control chart is a principal tool of on-line quality improvement. The implementation of process control should include analyzing the control chart pattern, diagnosing the troubles, formulating the adjust actions, and taking the adjust actions. Unfortunately, so far the decision support system for process control is limited to data storage, statistical analysis, control chart operation, etc. Therefore, the judgement and diagnosis for problems still depend on the experts, i.e. quality staffs. Using the concept of fuzzy logic, this study proposed a method to support the analysis of control chart pattern and diagnosis for the process problems. A prototype of real-time feedback control chart diagnosis system is developed for a casting plant. It provides the function of plotting, analyzing and diagnosing the X-bar chart.
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吳崑旭. "= Economic control np-chart desings for CHIU." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/21858623523623699165.
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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.
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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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Hsu, Po-chun, and 許博淳. "The Parameter Optimization of Zone Control Chart." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/62717050241624122684.
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碩士南台科技大學
工業管理研究所
98
In today's competitive system, production technology is getting riser and consumer’s quality requirement is significantly increasing. In the crisis surrounding environment, the product quality is a major factor for enterprises to remain competitiveness and keep going in the competitive world. Statistical process control (SPC) is one of important tools on quality control where control chart is the most widely used tool. Zone Control Chart (ZCC) is primarily used to replace the traditional Shewhart control chart for its insensitivity and Runs Rules Tests for its inconvenience. Zone control chart divides X control chart into four regions where each region is given a score such as 1, 2, 4, and 8. The purpose of this study is to search for the optimal parameter values for zone control chart. The research applied Central Composite Design (CCD) and Box-Behnken Design (BBD) to search for the best parameter values of zone control chart. The application of CCD was divided into two parts. The first part is to put the uncoded parameter values and SAS simulated ARL values into artificial neural network to find out the weights in the artificial neural network. Next let the set of weights as the initial weights in the genetic algorithms to search for the best weights. Then find the optimal parameter values for zone control chart and its corresponding ARL forecasts. Finally input the optimal parameter values to simulate the ARL values of zone control chart by SAS program and compare the differences between the two set of ARL values. The second part applied response surface methodology (RSM) and desirability function to obtain the optimal parameter values of zone control chart. Similarly, compare the differences between predicted ARLs and simulated ARLs. The same procedure was applied on data generated by Box-Behnken design.
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Huangshao-po and 黃卲博. "Nonparametric Control Chart Based on Data Depth." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/wstzns.
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Lee, Shao-Chen, and 李紹成. "Constructing Weibull Percentile Control Chart Using BLIEs." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2naf6n.
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碩士國立交通大學
工業工程與管理系所
107
In the stress analysis or reliability test of the product, the obtained stress data or reliability data usually presents the Weibull distribution, and the low percentile, which follow a non-normal distribution, is usually used to represent the weakest point of the material. If the traditional Shewhart control chart is used to monitor the percentile of the Weibull process, it may lead to misjudgment. To solve this problem, this study firstly estimates the low percentile of the Weibull process using the Best Linear Invariant Estimators (BLIEs) method, and then constructs the PB and the BCa confidence interval method in nonparametric Bootstrap method. The Weibull low-percentile control chart and sensitivity analysis prove that the Bootstrap low-percentile control chart proposed by this study is more effective than the traditional Shewhart control chart.
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Lin, Yun-Hong, and 林運鴻. "The study of multinomial proportion control chart." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/89567941529507596427.
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碩士銘傳大學
應用統計資訊學系碩士班
103
Traditional attributes control charts only monitor defect ratio of products using Binomial distribution. Recently, Lu et al. (1998) brought up Multivariate np control chart to monitor defect ratio of products. The classification of quality characteristics involves not only defect-free ratio or defect ratio but also levels. Traditional attributes control charts are not useful. Therefore, we bring up the method to resolve the issue of the multivariate quality characteristics using MNP control chart. Furthermore, we bring up two control charts. One is chi square control chart by goodness of fit. The other one is multivariate exponentially weighted moving average Kullback Leibler divergence control chart by Kullback Leibler divergence. Finally, we simulate the three control chart’s performance when manufacturing process has shifted.
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Huang, Bing-Chang, and 黃炳昌. "Adjusted X control chart by Edgeworth Expansion." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/08320824116741524289.
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碩士銘傳大學
應用統計資訊學系碩士班
103
X control chart is powerful tool to monitor the process mean, but it is not good detector when the process distribution is unknown. Zou and Wang(2005) propose a robust X control chart on the Cornish-Fisher expansion. They compare various control charts when parameter is known. However, there is no information about process parameters in some situations. Hence, we propose some estimates for parameter to adjust the X control chart. The new Edgeworth control charts are compared by simulation with Shewhart X control chart, Weighted-Variance control chart and Percentile Bootstrap control chart.
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Ting-WeiLi and 李庭媁. "An information-theoretical based process control chart." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/32472896996442951437.
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碩士國立成功大學
工業與資訊管理學系
102
The objective of this study is to construct a new type of control chart based on Kullback-Leibler distance of information theory. We name this control chart Information-Theoretical Based Process Control Chart. This study considers detecting the mean shift of a normally distributed process given that process variance is consistent. We use a statistic which is a derivation of Kullback-Leibler distance to construct the Information-Theoretical Based Process Control Chart. The control limits of the chart are obtained by simulation with 100,000 runs when in-control average run length is 500. Using the control limits acquired above, we get the out of control average run length for various shift sizes. The performance of control charts are measured by average run length in this study. The results of Information-Theoretical Based Process Control Chart are compared with cumulative sum (CUSUM), exponentially weighted moving average (EWMA), and generalized likelihood ratio (GLR) control chart. The Information-Theoretical Based Process Control Chart is effective in detecting shift of small sizes, but less effective in detecting shift of large sizes. The results are close to the CUSUM and EWMA control charts. It is shown that the overall performance of Information-Theoretical Based Process Control Chart is good. An advantage of Information-Theoretical Based Process Control Chart is that it does not require users to specify control chart parameters while CUSUM and EWMA control chart have to in order to detecting a specific mean shift faster.
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Huang, Hui-Kuo, and 黃惠國. "Robust Multivariate Control Chart for Outlier Detection." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/09160319863093273592.
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碩士元智大學
工業工程與管理學系
99
The aim of this thesis is to design two new multivariate control charts that can effectively detect potential outlier(s) in multi-dimensional data while keeping the masking and swamping effects under control. Predicted squared error and hierarchical cluster tree are augmented into the proposed control charts, respectively, to improve the Sullivan and Woodall second approach (SW2). Multivariate historical datasets are borrowed to illustrate the performance comparisons between various methods. A simulation study based on Monte Carlo experiments further verifies that the proposed method is more robust and 60-100 times faster than MVE in computation time for outlier detection than existing methods in the literature.
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Chiu, Chih-jung, and 邱致榮. "A statistical design for P control chart." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/58278660950427006066.
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碩士逢甲大學
工業工程與系統管理學研究所
98
The average run length is an index to present the ability of process-diagnosis by the control chart. Under the normal process, the average number of sampling, used to identify that the process is out-of-control in the control chart, is called the average run length ARL0. Under the abnormal process, the average number of sampling, used to identify that the process is out-of-control in the control chart, is called the average run length ARL1. The ARL0 of the control chart is the larger the better and ARL1 is the smaller the better. The purpose of this paper is to look for the parameters of the P control chart that make the ARL0 largest and the ARL1 smallest. The parameters of the P control chart that affect the average run length are sample size, sampling intervals, control limit coefficient, out-of-control probability and shift amount of defective proportion. These parameters are arranged by central composite design which includes 47 treatment combinations. Computer simulation each treatment combination, we get 47 pairs of ARL0 and ARL1 which use to fit the second-order response surface model of ARL0 and ARL1. And finally, maximize the difference between the ARL0 and ARL1 use partial differential to get the best parameter combination of the P control chart.