• Communications for Statistical Applications and Methods
• /
• v.6 no.2
• /
• pp.413-422
• /
• 1999

Multivariate EWMA control charts to simultaneously monitor correlation coefficients of correlated quality characteristics under multivariate normal process are proposed. Performances of the proposed charts are measured in terms of average run length(ARL). Numerical results show that smalle values for smoothing constant with accumulate-combine approach are preferred for detecting smalle shifts.

• Journal of the Korean Data and Information Science Society
• /
• v.10 no.1
• /
• pp.223-232
• /
• 1999

Multivariate quality control charts with combine-accumulate approach and accumulate-combine apprach for monitoring both means and variances under multivariate normal process are investigated. Numerical performances of the charts show that multivariate EWMA chart with accumulate-combine approach can be recommended for all kinds of shift in means and variances.

• Journal of the Korean Data and Information Science Society
• /
• v.16 no.3
• /
• pp.639-650
• /
• 2005

Theoretical and numerical comparison have shown that variable sampling interval (VSI) charts are substantially more efficient than fixed sampling interval(FSI) charts in term of ATS(average time to signal). But the frequency of switching between different sampling intervals is a complicating factor in VSI procedures. VSI EWMA charts for monitoring mean vector of related qualify characteristics are investigated. To compare the efficiencies of the proposed charts, the performances are evaluated for matched FSI and VSI charts in terms of average time to signal(ATS) and average number of samples to signal(ANSS). For the switching behavior of the proposed VSI charts, average number of switches(ANSW) are also investigated.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.6
• /
• pp.999-1008
• /
• 2010

The objective of this paper is to develop variable sampling interval multivariate control charts that can offer significant performance improvements compared to standard fixed sampling rate multivariate control charts. Most research on multivariate control charts has concentrated on the problem of monitoring the process mean, but here we consider the problem of simultaneously monitoring both the mean and variability of the process.

• Journal of Integrative Natural Science
• /
• v.7 no.3
• /
• pp.200-207
• /
• 2014

Average number of samples to signal (ANSS) and average time to signal (ATS) are the most widely used criterion for comparing the efficiencies of the quality control charts. In this study the method of evaluating ANSS and ATS values of the multivariate exponentially weighted moving average (EWMA) control charts with Markov chain approach was presented when the production process is in control state or out of control state. Through numerical results, it is found that when the number of transient state r is less than 50, the calculated ANSS and ATS values are unstable; and ATS(r) tends to be stabilized when r is greater than 100; in addition, when the properties of multivariate EWMA control chart is evaluated using Markov chain method, the number of transient state r requires bigger values when the smoothing constatnt ${\lambda}$ becomes smaller.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1993.10a
• /
• pp.31-39
• /
• 1993

This paper considers a procedure for the economic design of a cumulative score(CUSCORE) control chart and more sensitive than X-type control chart for small shift to control the mean of a process with a exponentially distributed quality characteristic. An expected loss - cost model as a function of design variables(sample size, sampling interval, scoring limit and decision limit) is derived. Direct search techniques are used to optimize the model subject to ARL in control. Numerical examples and sensitivity analysis of the model are presented. For selected values of situation parameters a comparison study with CUSUM charts is given. CUSCORE control charts compare favourably with CUSUM charts in cost for speedy production process. The proposed control chart can be directly applied for controlling the lifetime characteristics.

• Clinical and Experimental Pediatrics
• /
• v.51 no.1
• /
• pp.26-32
• /
• 2008

Purpose : This study was to provide the methods of developing the growth charts and the blood pressure nomogram among Korean children and adolescents. Methods : The growth charts were developed based on the data from the national growth surveys for children and adolescents in 1998 and 2005. The percentile charts were developed through two stages. At the first stage, the selected empirical charts were smoothed through several fitting procedures including parametric and non-parametric methods. At the second stage, a modified LMS (lambda, mu, sigma) statistical procedure was applied to the smoothed percentile charts. The LMS procedure allowed to estimate any percentile and to calculate standard deviation units and z-scores. The charts for weight-for-age, height-for-age, BMI-for-age, weight-for-height and head circumference-for-age were developed by sex. Age and normalized height controlled sex-specific nomograms of systolic and diastolic blood pressure were developed by a fixed effect model of general regression using the data from 2005 national growth survey. Results : The significant systemic differences between the percentiles of growth charts and the empirical data were not found. The final output of the study is available from Korean Center for Disease Control and Prevention homepage, http://www.cdc.go.kr/webcdc/. Blood Pressure nomogram was tabulated by height percentiles and age using the regression coefficients analyzed with regression model. Conclusion : 2007 growth charts and blood pressure nomogram were the first products based on the statistical modeling using the national survey data. The further study on the methodology including data collection, data cleaning and statistical modeling for representative growth charts would be needed.

• Communications for Statistical Applications and Methods
• /
• v.4 no.3
• /
• pp.715-723
• /
• 1997

Multivariate control statistics to simultaneously monitor both means and variances for several quality variables under multivariate normal process are proposed. Performances of the proposed multivariate charts are evaluated in terms of average run length(ARL). Multivariate Shewhart chart is also proposed to compare the performances of multivariate exponentially weighted moving average(EWMA) charts. A numerical comparison shows that multivariate EWMA charts are more efficient than multivariate Shewhart chart for small and moderate shifts and multivariate EWMA scheme based on accumulate-combine approach is more efficient than corresponding multivariate EWMA chart based on combine-accumulate approach.

• Journal of Korean Institute of Industrial Engineers
• /
• v.27 no.3
• /
• pp.289-296
• /
• 2001

This paper proposes multivariate control chart for autocorrelated data which are common in chemical and process industries and lead to increase in the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a vector autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and therefore the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and Monte Carlo simulation is conducted to investigate the performances of the proposed control charts.

• Journal of Korean Society for Quality Management
• /
• v.25 no.1
• /
• pp.100-115
• /
• 1997

Shewhart control chart is a basic technique to monitor the state of a process. We observe samples of size four or five and plot some statistic(e.g., mean or range) of each sample on the chart. When setting up the chart, we need to obtain u, pp.r and lower control limits. It is common practice that those limits are calculated from the preliminary 20-40 samples presumed to be homogeneous. However, it may ha, pp.n in practice that the samples are contaminated by outlying observations caused by various reasons. The presence of outlying observations make the control limits wider and hence decrease the sensitivity of the charts. In this paper, we introduce robust control charts with tighter control limits when outlying observations are present in the preliminary samples. Examples will be given via simulation study.