Using a new VSI EWMA average loss control chart to monitor changes in the difference between the process mean and target and/or the process variability

This investigation elucidates the feasibility of monitoring a process for which observational data are largely autocorrelated. Special causes typically affect not only the process mean but also the process variance. The EWMA control chart has recently been developed and adopted to detect small shifts in the process mean and/or variance. This work extends the EWMA control chart, called the generally weighted moving average (GWMA) control chart, to monitor a process in which the observations can be regarded as a first-order autoregressive process with a random error. The EWMA and GWMA control charts of residuals used to monitor process variability and to monitor simultaneously the process mean and variance are considered to evaluate how average run lengths (ARLs) differ in each case.

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A NEW VARIABLES CONTROL CHART FOR SIMULTANEOUSLY MONITORING MULTIVARIATE PROCESS MEAN AND VARIABILITY

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539302000652 ◽

2002 ◽

Vol 09 (01) ◽

pp. 41-59 ◽

Cited By ~ 24

Author(s):

ARTHUR B. YEH ◽

DENNIS K. J. LIN

Keyword(s):

Control Chart ◽

Integral Transformation ◽

Real Life ◽

Dimensional Control ◽

Process Mean ◽

Process Variability ◽

Uniform Distributions ◽

Probability Integral Transformation ◽

Probability Integral ◽

New Variables

In this paper, we propose a new variables control chart, called the box-chart, to simultaneously monitor, on a single chart, the process mean and process variability for multivariate processes. The box-chart uses a probability integral transformation to obtain two independently and identically distributed uniform distributions. Therefore, a box-shaped (thus the name), two-dimensional control chart can be constructed. We discuss in detail on how to construct the box-chart. The proposed chart is applied to two real-life examples. The performance of the box-chart is also compared to that of the traditional T2- and |S|-charts.

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A New EWMA Loss Control Chart with Adaptive Control Scheme

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.631-632.12 ◽

2014 ◽

Vol 631-632 ◽

pp. 12-17 ◽

Cited By ~ 1

Author(s):

Chung Ming Yang ◽

Su Fen Yang ◽

Jeng Sheng Lin

Keyword(s):

Adaptive Control ◽

Control Chart ◽

Control Charts ◽

Sampling Interval ◽

Average Loss ◽

The Difference ◽

And Performance ◽

Process Measurements ◽

Fixed Sampling Interval ◽

Adaptive Control Charts

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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An Average Loss Control Chart Under a Skewed Process Distribution

Frontiers in Statistical Quality Control 13 ◽

10.1007/978-3-030-67856-2_5 ◽

2021 ◽

pp. 65-76

Author(s):

Su-Fen Yang ◽

Shan-Wen Lu

Keyword(s):

Control Chart ◽

Average Loss ◽

Loss Control

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A new adaptive control chart for monitoring process mean and variability

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-011-3662-2 ◽

2011 ◽

Vol 60 (9-12) ◽

pp. 1031-1038 ◽

Cited By ~ 12

Author(s):

Jiujun Zhang ◽

Zhonghua Li ◽

Zhaojun Wang

Keyword(s):

Adaptive Control ◽

Control Chart ◽

Process Mean ◽

Monitoring Process ◽

Adaptive Control Chart

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PENERAPAN DIAGRAM KONTROL MEWMA DAN MEWMV PADA PENGENDALIAN KARAKTERISTIK KUALITAS AIR (Studi Kasus: Kualitas Pengolahan Air II PDAM Tirta Moedal Kota Semarang)

Jurnal Gaussian ◽

10.14710/j.gauss.v7i1.26632 ◽

2018 ◽

Vol 7 (1) ◽

pp. 23-32

Author(s):

Adestya Ayu Maharani ◽

Mustafid Mustafid ◽

Sudarno Sudarno

Keyword(s):

Water Treatment ◽

Control Chart ◽

Moving Average ◽

Process Capability ◽

Human Consumption ◽

Exponentially Weighted Moving Average ◽

Process Mean ◽

Monitoring Process ◽

Weighted Moving Average ◽

Exponentially Weighted

Water is one of the most important elements for human life, water treatment is done for human consumption and must fulfill the health requirements with the levels of certain parameters. Quality of Water Treatment II is the second water purification installation owned by PDAM Tirta Moedal Semarang City with production capacity of 60 l/s. Variables used in the water treatment process are correlated with each other, so used multivariate control chart. The Multivariate Exponentially Weighted Moving Average control chart is used for monitoring process mean, and the Multivariate Exponentially Weighted Moving Variance control chart is used for monitoring process variability. The variables used are colour, turbidity, organic substance, manganese and the total dissolved solid. MEWMA control chart with λ = 0.5, showed that the process mean is controlled statistically. MEWMV control chart showed that variability is controlled statistically in λ = 0.4, ω = 0.2 and L = 3.3213. MEWMA and MEWMV control chart showed that the process is not capable because it obtained the value of process capability index less than 1. Keywords: Water, Multivariate Exponentially Weighted Moving Average, Multivariate Exponentially Weighted Moving Variance, process capability.

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