• Title/Summary/Keyword: EWMA Control

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Modifications of single and double EWMA feedback controllers for balancing the mean squared deviation and the adjustment variance (편차제곱평균과 수정량분산의 균형을 위한 단일 및 이중 지수가중이동평균 피드백 수정기의 수정)

  • Park, Chang-Soon;Kwon, Sung-Gu
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.1
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    • pp.11-24
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    • 2009
  • The process controller in the adjustment procedure is often used effectively to control the process level close to target when noise is present and unremovable. Examples of the robust controller are single EWMA controller and double EWMA controller. Double EWMA controller is designed to reduce the offset of the process deviation, which single EWMA can not eliminate. In this paper, the two controllers are modified by taking EWMA of the original controller to reduce the adjustment variance, which may become excessively large when the two given controllers are implemented. It is shown that the EWMA modification of the given controllers is successful in reducing the adjustment variance, while the mean squared deviation increases slightly.

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Multivariate Control Charts for Several Related Quality Characteristics

  • Chang, Duk-Joon;Shin, Jae-Kyoung
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.2
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    • pp.467-476
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    • 2005
  • Multivariate control charts for monitoring mean vector of several related quality variables with combine-accumulate approach and accumulate-combine apprach were investigated. Shewhart chart is also proposed to compare the performances of CUSUM and EWMA charts. Numerical comparisons show that CUSUM and EWMA charts are more efficient than Shewhart chart for small or moderate shifts, and multivariate charts based on accumulate- combine approach is more efficient than corresponding multivariate charts based on combine-accumulate approach.

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Multivariate EWMA Control Charts for Monitoring Dispersion Matrix

  • Chang Duk-Joon;Lee Jae Man
    • Communications for Statistical Applications and Methods
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    • v.12 no.2
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    • pp.265-273
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    • 2005
  • In this paper, we proposed multivariate EWMA control charts for both combine-accumulate and accumulate-combine approaches to monitor dispersion matrix of multiple quality variables. Numerical performance of the proposed charts are evaluated in terms of average run length(ARL). The performances show that small smoothing constants with accumulate-combine approach is preferred for detecting small shifts of the production process.

Investigate Study on the relation between Multivariate SPC and Autoregressed Algorithm (다변량 SPC와 자기회귀알고리즘의 연계를 위한 조사연구)

  • Jung, Hae-Woon
    • Proceedings of the Safety Management and Science Conference
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    • 2011.04a
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    • pp.675-693
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    • 2011
  • We compare three Techniques control systems with The Investigate Study on the relation between Multivariate SPC and Autoregressed Algorithm. We also investigate Autoregressed Algorithm with relevant EWMA, CUSUM, Shewhart chart, Precontrol chart and Process Capacity.

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EWMA control chart for Katz family of distributions (카즈분포족에 대한 지수가중이동평균관리도)

  • Cho, Gyo-Young
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.4
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    • pp.681-688
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    • 2010
  • In statistical process control, the primary method used to monitor the number of nonconformities is the c-chart. The conventional c-chart is based on the assumption that the occurrence of nonconformities in samples is well modeled by a Poisson distribution. When the Poisson assumption is not met, the X-chart is often used as an alternative charting scheme in practice. And EWMA control chart is used when it is desirable to detect out-of-control situations very quickly because of sensitive to a small or gradual drift in the process.

An Economic Design of the Integrated Process Control Procedure with Repeated Adjustments and EWMA Monitoring

  • Park Changsoon;Jeong Yoonjoon
    • Proceedings of the Korean Statistical Society Conference
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    • 2004.11a
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    • pp.179-184
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    • 2004
  • Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for process quality improvement. SPC reduces process variability by detecting and eliminating special causes of process variation, while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been need for an integrated process control (IPC) procedure which combines the two strategies. This article considers a scheme that simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process disturbance model under consideration is an IMA(1,1) model with a location shift. The EPC part of the scheme adjusts the process, while the SPC part of the scheme detects the occurrence of a special cause. For adjusting the process repeated adjustment is applied by compensating the predicted deviation from target. For detecting special causes the two kinds of exponentially weighted moving average (EWMA) control chart are applied to the observed deviations: One for detecting location shift and the other for detecting increment of variability. It was assumed that the adjustment of the process under the presence of a special cause may change any of the process parameters as well as the system gain. The effectiveness of the IPC scheme is evaluated in the context of the average cost per unit time (ACU) during the operation of the scheme. One major objective of this article is to investigate the effects of the process parameters to the ACU. Another major objective is to give a practical guide for the efficient selection of the parameters of the two EWMA control charts.

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EWMA chart Application using the Transformation of the Exponential with Individual Observations (개별 관측치에서 지수변환을 이용한 EWMA 관리도 적용기법)

  • 지선수
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.22 no.52
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    • pp.337-345
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    • 1999
  • The long-tailed, positively skewed exponential distribution can be made into an almost symmetric distribution by taking the exponent of the data. In these situations, to use the traditional shewhart control limits on an individuals chart would be impractical and inconvenient. The transformed data, approximately bell-shaped, can be plotted conveniently on the individuals chart and exponentially weighted moving average chart. In this paper, using modifying statistics with transformed exponential of the data, we give a method for constructing control charts. Selecting method of exponent for individual chart is evaluated. And consider that smaller weight being assigned to the older data as time process and properties and taking method of exponent($\theta$), weighting factor($\alpha$) are suggested. Our recommendation, on the basis result of simulation, is practical method for EWMA chart.

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EWMA Control Charts to Monitor Correlation Coefficients

  • Chang, Duk-Joon;Cho, Gyo-Young;Lee, Jae-Man
    • Communications for Statistical Applications and Methods
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    • v.6 no.2
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    • pp.413-422
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    • 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.

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A Combined Process Control Procedure by Monitoring and Repeated Adjustment

  • Park, Changsoon
    • Communications for Statistical Applications and Methods
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    • v.7 no.3
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    • pp.773-788
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    • 2000
  • Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for processes quality improvement. SPC reduces process variability by detecting and eliminating special causes of process variation. while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been needs for a process control proceduce which combines the tow strategies. This paper considers a combined scheme which simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an integrated moving average(IMA) process with a step shift. The EPC part of the scheme adjusts the process back to target at every fixed monitoring intervals, which is referred to a repeated adjustment scheme. The SPC part of the scheme uses an exponentially weighted moving average(EWMA) of observed deviation from target to detect special causes. A Markov chain model is developed to relate the scheme's expected cost per unit time to the design parameters of he combined control scheme. The expected cost per unit time is composed of off-target cost, adjustment cost, monitoring cost, and false alarm cost.

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