• Title/Summary/Keyword: EWMA Control

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Development of CV Control Chart Using EWMA Technique (EWMA 기법을 적용한 CV 관리도의 개발)

  • Hong, Eui-Pyo;Kang, Chang-Wook;Baek, Jae-Won;Kang, Hae-Woon
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.31 no.4
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    • pp.114-120
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    • 2008
  • The control chart is widely used statistical process control(SPC) tool that searches for assignable cause of variation and detects any change of process. Generally, ${\bar{X}}-R$ control chart and ${\bar{X}}-S$ are most frequently used. When the production run is short and process parameter changes frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart is an effective tool to control the mean and variability of process simultaneously. The CV control chart, however, is not sensitive at small shift in the magnitude of CV. In this paper, we propose an CV-EWMA (exponentially weighted moving average) control chart which is effective in detecting a small shift of CV. Since the CV-EWMA control chart scheme can be viewed as a weighted average of all past and current CV values, it is very sensitive to small change of mean and variability of the process. We suggest the values of design parameters and show the results of the performance study of CV-EWMA control chart by the use of average run length (ARL). When we compared the performance of CV-EWMA control chart with that of the CV control chart, we found that the CV-EWMA control chart gives longer in-control ARL and much shorter out-of-control ARL.

An Economic Design of the EWMA Control Charts with Variable Sampling Interval (VSI EWIMA 관리도의 경제적 설계)

  • 송서일;정혜진
    • Journal of Korean Society for Quality Management
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    • v.30 no.4
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    • pp.1-14
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    • 2002
  • Traditional SPC techniques are looking out variation of process by fixed sampling interval and fixed sample size about every hour, the process of in-control or out-of-control couldn't be detected actually when the sample points are plotted near control limits, and it takes no notice of expense concerned with such sample points. In this paper, to overcome that, consider VSI(variable sampling interval) EWMA control charts which VSI method is applied. The VSI control charts use a short sampling internal if previous sample points are plotted near control limits, then the process has high probability of out-of-control. But it uses a long sampling interval if they are plotted near centerline of the control chart, since process has high possibility of in-control. And then a comparison and analysis between FSI(fixed sampling interval) and VSI EWMA in the statistical aspect and economic aspect is studied. Finally, we show that VSI EWMA control chart is more efficient than FSI EWMA control chart in the both aspects.

A Study on the Design of Adaptive EWMA Control Chart using Kalman Gain Recursive Average (칼만 게인 궤환 평균을 이용한 적응 EWMA 관리도 설계)

  • Yoon, Sangwon;Yoon, Seokhwan;Shin, Yongback
    • Journal of Korean Society for Quality Management
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    • v.24 no.1
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    • pp.73-86
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    • 1996
  • Adaptive EWMA(Exponentially Weighted Moving Average)-x control chart using the Kalman gain recursive average is designed. The designed control chart is effective to on-line process monitoring as continuous flow processes. Performance evaluation between the designed control chart and traditional one is implemented. For this, ARL(Average Run Length) is adopted as a criterion. Results show that the designed adaptive EWMA-x control chart has shorter ARL than EWMA-x control chart when process mean is shifted. This model can be extended to process prevention control. The methodology proposed in this research is turned out to show the high performance than that of the given methodologies.

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A Robust EWMA Control Chart (로버스트 지수가중 이동평균(EWMA) 관리도)

  • Nam, Ho-Soo;Lee, Byung-Gun;Joo, Cheol-Min
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.1
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    • pp.233-241
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    • 1999
  • Control chart is a very extensively used tool in testing whether a process is in a state of statistical control or not. In this paper, we propose a robust EWMA(exponentially weighted moving averages) control chart for variables, which is based on the Huber's M-estimator. The Huber's M-estimator is a well-known robust estimator in sense of distributional robustness. In the proposed chart, the estimation of the process deviation is modified to have a s table level and high power. To compare the performances of the proposed control chart with other charts, some Monte Carlo simulations we performed. The simulation results show that the robust EWMA control chart has good performance.

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A Study on the Multivariate Exponentially Weighted Moving Average Control Charts for Monitoring the Variance-Covariance Matrix

  • Cho, Gyo-Young;Sung, Sam-Kyung
    • Journal of Korean Society for Quality Management
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    • v.22 no.1
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    • pp.54-65
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    • 1994
  • Multivariate exponentially weighted moving average (EWMA) control charts for monitoring the variance-covariance matrix are investigated. Two basic approaches, "combine-accumulate" approach and "accumulate-combine" approach, for using past sample information in the developement of multivariate EWMA control charts are considered. Multivariate EWMA control charts for monitoring the variance-covariance matrix are compared on the basis of their average run length (ARL) performances. The numerical results show that multivariate EWMA control charts based on the accumulate-combine approach are more efficient than corresponding multivariate EWMA control charts based on the combine-accumulate approach.

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Multivariate EWMA control charts for monitoring the variance-covariance matrix

  • Jeong, Jeong-Im;Cho, Gyo-Young
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.4
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    • pp.807-814
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    • 2012
  • We know that the exponentially weighted moving average (EWMA) control charts are sensitive to detecting relatively small shifts. Multivariate EWMA control charts are considered for monitoring of variance-covariance matrix when the distribution of process variables is multivariate normal. The performances of the proposed EWMA control charts are evaluated in term of average run length (ARL). The performance is investigated in three types of shifts in the variance-covariance matrix, that is, the variances, covariances, and variances and covariances are changed respectively. Numerical results show that all multivariate EWMA control charts considered in this paper are effective in detecting several kinds of shifts in the variance-covariance matrix.

Optimal Design of a EWMA Chart to Monitor the Normal Process Mean

  • Lee, Jae-Heon
    • The Korean Journal of Applied Statistics
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    • v.25 no.3
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    • pp.465-470
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    • 2012
  • EWMA(exponentially weighted moving average) charts and CUSUM(cumulative sum) charts are very effective to detect small shifts in the process mean. These charts have some control-chart parameters that allow the charts and be tuned and be more sensitive to certain shifts. The EWMA chart requires users to specify the value of a smoothing parameter, which can also be designed for the size of the mean shift. However, the size of the mean shift that occurs in applications is usually unknown and EWMA charts can perform poorly when the actual size of the mean shift is significantly different from the assumed size. In this paper, we propose the design procedure to find the optimal smoothing parameter of the EWMA chart when the size of the mean shift is unknown.

An Economic Statistical Design of the EWMA Control Charts with Variable Sampling Interval (VSI EWMA 관리도의 경제적 통계적 설계)

  • 송서일;박현규;정혜진
    • Journal of Korean Society for Quality Management
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    • v.32 no.1
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    • pp.92-101
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    • 2004
  • Tris paper present an economic statistical design which have statistically constraints for the optimal design of an EWMA control charts with variable sampling interval. Cost function use that proposed by Lorenzen and Vance, and the optimal design parameters include the sample size, control limit width, sampling interval, EWMA weight value. Comparisons between VSI EWMA control charts optimal economic design and optimal economic statistical designs show the following fact. Although have demerits which are more costly than economic design, have merits which to detect shifts more efficiently and to improve statistical performance.

EWMA Control Charts with Variable Parameter (가변모수를 갖는 EWMA 관리도)

  • Lee, Jae-Heon;Han, Jung-Hee
    • Journal of Korean Society for Quality Management
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    • v.33 no.4
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    • pp.117-122
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    • 2005
  • Variable sampling rate(VSR) scheme varies the sampling rate for the current sample depending on the previous value of the control statistic. In this paper, we propose EWMA control charts with variable parameter(VP) scheme, which allows both the sample rate(the sample size or the sampling interval) and the weight to vary. We investigate the effectiveness of the VP scheme relative to the fixed parameter(FP) scheme and the VSR scheme in EWMA control charts. It is shown that using the VP scheme gives some improvements to the ability in detecting small and moderate shifts in the process normal mean.

EWMA Control Chart for Monitoring a Process Correlation Coefficient (상관계수의 변동을 탐지하기 위한 EWMA 관리도)

  • 한정혜;조중재
    • Journal of Korean Society for Quality Management
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    • v.26 no.1
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    • pp.108-125
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    • 1998
  • The EWMA(Exponentially Weighted Moving Average) has recently received a great deal of attention in the quality control literature as a process monitoring tool on the shop floor of manufacturing industires, since it is easy to plot, to interpret, and its control limits are easy to obtain. Most a, pp.ications of the EWMA for process monitoring have concentrated on the problem of detecting shifts of a process mean and a process standard deviation with ARL(Average Run Length) properties. But there may be the necessity of controlling linearity on product quality such as the correlation coefficient to the process operator. Control managers may want to protect the increase of a process correlation coefficient value, such as 0, between two variables of interest. However, there are few studies concerned on this part. Therefore, we propose EWMA models for a process correlation coefficient using two transformed statistics, T-statistic and (Fisher's) Z-statistic. We also present some results of simulation by SAS/IML and compare two models.

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