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Relevant bibliographies by topics / Chart of control

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Academic literature on the topic 'Chart of control'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Chart of control"

1

Yang, Chung Ming, Su Fen Yang, and Jeng Sheng Lin. "A New EWMA Loss Control Chart with Adaptive Control Scheme." Applied Mechanics and Materials 631-632 (September 2014): 12–17. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.12.

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A single chart, instead of and R charts or and S charts, to simultaneously monitor the process mean and variability would reduce the required time and effort. A number of studies have attempted to find such charts. Moreover, a number of studies demonstrated that the adaptive control charts may detect process shifts faster than the fixed control charts. This paper proposes the EWMA loss chart with variable sample sizes and sampling intervals (VSSI) to effectively monitor the difference of process measurements and target. An example is used to illustrate the application and performance of the proposed control chart in detecting the changes in the difference of the process measurements and target. Numerical analyses demonstrated that the VSSI EWMA loss chart outperforms the fixed sampling interval EWMA average loss chart and the Shewhart joint and S charts. Therefore, the VSSI EWMA loss chart is recommended.

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Ramirez-Mendez, Esmeralda, and Mario Cantu-Sifuentes. "Multiatributte Double Sampling Control Chart." Industrial and Systems Engineering Review 2, no. 1 (July 8, 2014): 42–51. http://dx.doi.org/10.37266/iser.2014v2i1.pp42-51.

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In recent years, multiattribute control charts have received an increasing attention. These charts are able to monitor two or more attributes in the same chart. In addition, there are many applications of multiatributte control charts in a wide variety of manufacturing processes and services. In this article, a multiattribute double sampling (DS D2) control chart is proposed. Double sampling is a methodology used to improve the efficiency of a control chart to detect quality issues without increase the sampling. Results of comparative studies via simulation indicate that the proposed control chart significantly outperforms in most of the supposed sceneries, in terms of the Average Run Length.

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Rashid, Kawa M. Jamal, and Suzan S. Haydar. "Construction of control charts by using Fuzzy Multinomial -FM and EWMA Chart “Comparative study"." Journal of Zankoy Sulaimani - Part A 16, no. 3 (July 3, 2014): 21–26. http://dx.doi.org/10.17656/jzs.10300.

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Elevli, Sermin, Nevin Uzgören, Deniz Bingöl, and Birol Elevli. "Drinking water quality control: control charts for turbidity and pH." Journal of Water, Sanitation and Hygiene for Development 6, no. 4 (September 26, 2016): 511–18. http://dx.doi.org/10.2166/washdev.2016.016.

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Water treatment processes are required to be in statistical control and capable of meeting drinking water specifications. Control charts are used to monitor the stability of quality parameters by distinguishing the in-control and out-of-control states. The basic assumption in standard applications of control charts is that observed data from the process are independent and identically distributed. However, the independence assumption is often violated in chemical processes such as water treatment. Autocorrelation, a measure of dependency, is a correlation between members of a series arranged in time. The residuals obtained from an autoregressive integrated moving averages (ARIMA) time series model plotted on a standard control chart is used to overcome the misleading of standard control charts in the case of autocorrelation. In this study, a special cause control (SCC) chart, also called a chart of residuals from the fitted ARIMA model, has been used for turbidity and pH data from a drinking water treatment plant in Samsun, Turkey. ARIMA (3,1,0) for turbidity and ARIMA (1,1,1) for pH were determined as the best time series models to remove autocorrelation. The results showed that the SCC chart is more appropriate for autocorrelated data to evaluate the stability of the water treatment process, since it provides a higher probability of coverage than an individual control chart.

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Liu, Jian, Kai Yun Yang, and Wei Wen. "A modified MEWMA control chart: PEWMA control chart." International Journal of Management Concepts and Philosophy 10, no. 2 (2017): 184. http://dx.doi.org/10.1504/ijmcp.2017.084052.

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Huay Woon, You. "A Comparative Analysis of Control Charts for Monitoring Process Mean." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 2616–22. http://dx.doi.org/10.17762/turcomat.v12i3.1263.

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Control charts serve as an effective tool for controlling and monitoring process quality in industries of production and service. The Shewhart chart is the first control chart that was used to detect large mean shifts in a process. Since then, to increase the Shewhart chart’s sensitivity, synthetic type control charts, such as synthetic control chart, side sensitive group runs (SSGR) control chart, have been proposed. SSGR chart ismore efficient compared to the Shewhart chart and synthetic chart,primarily due to the side sensitive feature in SSGR chart. Meanwhile, exponentially weighted moving average (EWMA) chart isoften used to detect small process changes. In practice, the evaluation of a control chart’s performance is vital. Nevertheless, the cost of implementing a control chart is an important factor that influences the choice of a control chart. The cost of repairs, sampling, nonconforming products from a failure in detecting out-of-control status, and investigating false alarms, can be significantly high. Therefore, the aim of this paper is to compare the implementation cost of synthetic, SSGR and EWMA charts, so that quality practitioners can identify the most cost-effective chart to implement. Here, the cost function was adopted to compute the implementation cost of the control chart. According to the findings, quality practitioners are recommended to adopt the SSGR chart,since it is more economical compared to the synthetic chart. However, the cost to implement anEWMA chart is higher than the synthetic and SSGR charts. In light of this, this study allows for quality practitioners to have a better idea on the selection of the control chart to implement, with respect to its cost.

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Gani, Walid, and Mohamed Limam. "On the Use of the K-Chart for Phase II Monitoring of Simple Linear Profiles." Journal of Quality and Reliability Engineering 2013 (June 5, 2013): 1–8. http://dx.doi.org/10.1155/2013/705450.

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Control charts for monitoring linear profiles are used to control quality processes which are characterized by a relationship between a response variable and one or more explanatory variables. In the literature, the majority of control charts deal with phase II analysis of linear profiles, where the objective is to assess the performance of control charts in detecting shifts in the parameters of linear profiles. Recently, the kernel distance-based multivariate control chart, also known as the K-chart, has received much attention as a promising nonparametric control chart with high sensitivity to small shifts in the process. Despite its numerous advantages, no work has proposed the use of the K-chart for monitoring simple linear profiles and that serves the motivation for this paper. This paper proposes the use of the K-chart for monitoring simple linear profiles. A benchmark example is used to show the construction methodology of the K-chart for simultaneously monitoring the slope and intercept of linear profile. In addition, performance of the K-chart in detecting out-of-control profiles is assessed and compared with traditional control charts. Results demonstrate that the K-chart performs better than the T2 control chart, EWMA control chart, and R-chart under small shift in the slope.

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Machado, Marcela A. G., and Antonio F. B. Costa. "The use of principal components and univariate charts to control multivariate processes." Pesquisa Operacional 28, no. 1 (April 2008): 173–96. http://dx.doi.org/10.1590/s0101-74382008000100010.

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In this article, we evaluate the performance of the T² chart based on the principal components (PC X chart) and the simultaneous univariate control charts based on the original variables (SU charts) or based on the principal components (SUPC charts). The main reason to consider the PC chart lies on the dimensionality reduction. However, depending on the disturbance and on the way the original variables are related, the chart is very slow in signaling, except when all variables are negatively correlated and the principal component is wisely selected. Comparing the SU , the SUPC and the T² charts we conclude that the SU X charts (SUPC charts) have a better overall performance when the variables are positively (negatively) correlated. We also develop the expression to obtain the power of two S² charts designed for monitoring the covariance matrix. These joint S² charts are, in the majority of the cases, more efficient than the generalized variance chart.

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Yang, Ming Jie, and Xue Min Zi. "The Comparison among Three Control Charts for Monitoring the Auto Correlated Processes." Applied Mechanics and Materials 490-491 (January 2014): 1579–83. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1579.

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We compare the ARL of three charts for monitoring the mean shifts of the first-order auto regressive model to choose a proper control chart. Simulation results show that the REWMA chart has a large superior to the EWMA and T2 the chart when -1<Ø<0, but when Ø>0, the chart is better than the other two charts.

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Abid, Muhammad, Hafiz Zafar Nazir, Muhammad Riaz, and Zhengyan Lin. "In-control robustness comparison of different control charts." Transactions of the Institute of Measurement and Control 40, no. 13 (November 1, 2017): 3860–71. http://dx.doi.org/10.1177/0142331217734302.

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Control charts are widely used to monitor the process parameters. Proper design structure and implementation of a control chart requires its in-control robustness, otherwise, its performance cannot be fairly observed. It is important to know whether a chart is sensitive to disturbances to the model (e.g. normality under which it is developed) or not. This study, explores the robustness of Mixed EWMA-CUSUM (MEC) control chart for location parameter under different non-normal and contaminated environments and compares it with its counterparts. The robustness of the MEC scheme and counterparts is evaluated by using the run length distributions, and for better assessment not only is in-control average run length (ARL) used, but also standard deviation of run length (SDRL) and different percentiles – that is, 5th, 50th and 95th– are considered. A careful insight is necessary in selection and application of control charts in non-normal and contaminated environments. It is observed that the in-control robustness performance of the MEC scheme is quite good in the case of normal, non-normal and contaminated normal distributions as compared with its competitor’s schemes.

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Dissertations / Theses on the topic "Chart of control"

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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.

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Zou, Xueli. "A robust Shewhart control chart adjustment strategy." Diss., This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-06062008-164701/.

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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 SUPERIOR
Os 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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Books on the topic "Chart of control"

1

Wheeler, Donald J. A Japanese control chart. Knoxville, Tennessee: SPC Press, 1986.

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Laha, Arnab Kumar. Sb-robustness of performance measures of control chart. Ahmedabad, India: Indian Institute of Management, 2013.

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ASTM Committee E-11 on Quality and Statistics, ed. Manual on presentation of data and control chart analysis. 8th ed. West Conshohocken, PA: ASTM International, 2010.

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Luko, Stephen N., ed. Presentation of Data and Control Chart Analysis, 9th Edition. 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959: ASTM International, 2018. http://dx.doi.org/10.1520/mnl7-9th-eb.

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Luhanga, Matthew Laban. Control system analysis and design using the Smith chart. New York: Wiley, 1990.

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Neubauer, Dean V., ed. Manual on Presentation of Data and Control Chart Analysis, 8th Edition. 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959: ASTM International, 2010. http://dx.doi.org/10.1520/mnl7-8th-eb.

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Mintzas, George. Development of control chart simulations for c and u type schemes. Manchester: UMIST, 1997.

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Henry, J. The chart of life, or The true theory of reproduction with rules: Respecting the control of offspring. New York: [s.n.], 1985.

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Flood, Mary Josephine. The relative merits of test procedure for location and dispersion characteristics with application to control chart analyses. [s.l: The Author], 1995.

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FAST creativity & innovation: Rapidly improving processes, product development and solving complex problems. Fort Lauderdale, Fla: J. Ross Pub., 2007.

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Book chapters on the topic "Chart of control"

1

Pham, Duc Truong, and Ercan Oztemel. "Control Chart Pattern Recognition." In Intelligent Quality Systems, 80–109. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-1498-7_4.

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Cheng, Smiley W., and Keoagile Thaga. "The Max-CUSUM Chart." In Frontiers in Statistical Quality Control 9, 85–98. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2380-6_6.

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

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Abbasi, Saddam Akber, and Arden Miller. "An Efficient Dispersion Control Chart." In Lecture Notes in Electrical Engineering, 61–70. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4786-9_5.

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Yu, Chun-yun, and Zhi-min Guan. "Direct Control-Chart of Substandard Products Control." In Proceedings of 2012 3rd International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012), 233–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33012-4_24.

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Wilkie, Jacqueline, Michael Johnson, and Reza Katebi. "Analysis and simple design using the Nichols chart." In Control Engineering, 505–28. London: Macmillan Education UK, 2002. http://dx.doi.org/10.1007/978-1-4039-1457-6_17.

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Bergman, B. "On an Improved Acceptance Control Chart." In Frontiers in Statistical Quality Control, 154–62. Heidelberg: Physica-Verlag HD, 1987. http://dx.doi.org/10.1007/978-3-662-11787-3_10.

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Arnold, B. F. "Minimax Principle and Control Chart Design." In Frontiers in Statistical Quality Control, 229–41. Heidelberg: Physica-Verlag HD, 1987. http://dx.doi.org/10.1007/978-3-662-11787-3_15.

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Bissell, Derek. "Getting more from control chart data." In Statistical Methods for SPC and TQM, 305–33. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-7120-3_16.

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Hoadley, Bruce, and Bill Huston. "The primal state adaptive control chart." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 146–63. Hayward, CA: Institute of Mathematical Statistics, 1986. http://dx.doi.org/10.1214/lnms/1215540296.

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Conference papers on the topic "Chart of control"

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Sihyun. Paik. "Demand Control Chart." In 2006 IEEE International Conference on Service Operations and Logistics, and Informatics. IEEE, 2006. http://dx.doi.org/10.1109/soli.2006.237149.

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Paik, Sihyun, and Sukchul Rim. "Demand Control Chart." In 2006 IEEE International Conference on Service Operations and Logistics, and Informatics. IEEE, 2006. http://dx.doi.org/10.1109/soli.2006.328952.

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ŞENTÜRK, SEVİL. "FUZZY REGRESSION CONTROL CHART." In Proceedings of the 8th International FLINS Conference. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812799470_0158.

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ERGİNEL, NİHAL. "FUZZY $\widetilde{p}$ CONTROL CHART." In Proceedings of the 8th International FLINS Conference. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812799470_0157.

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Zhang, X., J. Liu, and C. Tan. "Multivariate Bayesian VSI control chart." In 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2015. http://dx.doi.org/10.1109/ieem.2015.7385603.

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Lestari, Tika, Khreshna Syuhada, and Utriweni Mukhaiyar. "Bivariate control chart with copula." In 1ST INTERNATIONAL CONFERENCE ON ACTUARIAL SCIENCE AND STATISTICS (ICASS 2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4936445.

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SHEU, SHEY-HUEI, and SHIN-LI LU. "THE AUTOCORRELATED GWMA CONTROL CHART." In Proceedings of the 2nd International Workshop (AIWARM 2006). WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773760_0093.

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Nugraha, Jaka, Is Fatimah, and Rino Galang Prabowo. "Control of wastewater using multivariate control chart." In PROCEEDINGS FROM THE 14TH INTERNATIONAL SYMPOSIUM ON THERAPEUTIC ULTRASOUND. Author(s), 2017. http://dx.doi.org/10.1063/1.4978199.

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Fong, Sze Jeeu, Sze San Nah, and Pooi Ah Hin. "New control chart for multivariate process." In 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE2012). IEEE, 2012. http://dx.doi.org/10.1109/icssbe.2012.6396609.

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Mohan, Ambili, Elizabeth Rita Samuel, and Gylson Thomas. "Helicopter Maneuverability Control Using Nichols Chart." In 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research (ICETIETR). IEEE, 2018. http://dx.doi.org/10.1109/icetietr.2018.8529107.

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Reports on the topic "Chart of control"

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Krishnaiah, P. R., and B. Q. Miao. Control Charts When the Observations Are Correlated. Fort Belvoir, VA: Defense Technical Information Center, May 1987. http://dx.doi.org/10.21236/ada186388.

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Arney, Chris, and Nicholas Howard. Information in Command and Control: Connecting Mission Command and Social Network Analysis (Briefing Charts). Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada607282.

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