scholarly journals Optimal parameters of EWMA Control Chart for Seasonal and Non-Seasonal Moving Average Processes

2021 ◽  
Vol 2014 (1) ◽  
pp. 012005
Author(s):  
Y Areepong ◽  
C Chananet
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Optimal Parameters ◽  
Ewma Control Chart ◽  
Moving Average Processes

scholarly journals Average Run Length on CUSUM Control Chart for Seasonal and Non-Seasonal Moving Average Processes with Exogenous Variables

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 173
Author(s):  
Rapin Sunthornwat ◽  
Yupaporn Areepong
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Moving Average ◽  
Optimal Parameters ◽  
Exogenous Variables ◽  
Explicit Formulas ◽  
Run Length ◽  
Ewma Control Chart ◽  
Cusum Control Chart ◽  
Moving Average Processes

The aim of this study was to derive explicit formulas of the average run length (ARL) of a cumulative sum (CUSUM) control chart for seasonal and non-seasonal moving average processes with exogenous variables, and then evaluate it against the numerical integral equation (NIE) method. Both methods had similarly excellent agreement, with an absolute percentage error of less than 0.50%. When compared to other methods, the explicit formula method is extremely useful for finding optimal parameters when other methods cannot. In this work, the procedure for obtaining optimal parameters—which are the reference value ( a ) and control limit ( h )—for designing a CUSUM chart with a minimum out-of-control ARL is presented. In addition, the explicit formulas for the CUSUM control chart were applied with the practical data of a stock price from the stock exchange of Thailand, and the resulting performance efficiency is compared with an exponentially weighted moving average (EWMA) control chart. This comparison showed that the CUSUM control chart efficiently detected a small shift size in the process, whereas the EWMA control chart was more efficient for moderate to large shift sizes.

A EWMA Control Chart for Exponential Distributed Quality Based on Moving Average Statistics

2015 ◽  
Vol 32 (3) ◽  
pp. 1179-1190 ◽  
Author(s):  
Nasrullah Khan ◽  
Muhammad Aslam ◽  
Chi-Hyuck Jun
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Ewma Control Chart

Double moving average–EWMA control chart for exponentially distributed quality

2017 ◽  
Vol 46 (9) ◽  
pp. 7351-7364 ◽  
Author(s):  
Muhammad Aslam ◽  
Wenhao Gui ◽  
Nasrullah Khan ◽  
Chi-Hyuck Jun
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Ewma Control Chart

An EWMA control chart based on the Wilcoxon rank-sum statistic using repetitive sampling

2018 ◽  
Vol 35 (3) ◽  
pp. 711-728 ◽  
Author(s):  
Jean-Claude Malela-Majika ◽  
Olatunde Adebayo Adeoti ◽  
Eeva Rapoo
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Moving Average ◽  
Location Parameter ◽  
Process Mean ◽  
Content Type ◽  
Underlying Process ◽  
Run Length ◽  
Design And Implementation ◽  
Ewma Control Chart

Purpose The purpose of this paper is to develop an exponentially weighted moving average (EWMA) control chart based on the Wilcoxon rank-sum (WRS) statistic using repetitive sampling to improve the sensitivity of the EWMA control chart to process mean shifts regardless of the prior knowledge of the underlying process distribution. Design/methodology/approach The proposed chart is developed without any distributional assumption of the underlying quality process for monitoring the location parameter. The authors developed formulae as well as algorithms to facilitate the design and implementation of the proposed chart. The performance of the proposed chart is investigated in terms of the average run-length, standard deviation of the run-length (RL), average sample size and percentiles of the RL distribution. Numerical examples are given as illustration of the design and implementation of the proposed chart. Findings The proposed control chart presents very attractive RL properties and outperforms the existing nonparametric EWMA control chart based on the WRS in the detection of the mean process shifts in many situations. However, the performance of the proposed chart relatively deteriorates for small phase I sample sizes. Originality/value This study develops a new control chart for monitoring the process mean using a two-sample test regardless of the nature of the underlying process distribution. The proposed control chart does not require any assumption on the type (or nature) of the process distribution. It requires a small number of subgroups in order to reach stability in the phase II performance.

Multi-Stage Health Diagnosis for Concrete Cable-Stayed Bridge Based on EWMA Control Chart

2012 ◽  
Vol 246-247 ◽  
pp. 903-907
Author(s):  
Zong Bao Liang ◽  
Ming Lan Sheng ◽  
Yi Ran Hu
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Element Model ◽  
Live Load ◽  
Load Effect ◽  
Structural Health ◽  
Cable Stayed Bridge ◽  
Ewma Control Chart ◽  
Multi Stage ◽  
Health Diagnosis

To overcome the huge difficulties in constructing an accurately finite element model in health diagnosis of bridge, this paper presented an improved diagnosis method via exponential weighted moving average (EWMA) control chart. Firstly the characteristic of the live-load effect of main girder strain in a bridge was analyzed as a theoretical basis of the proposed strategy. Then wavelet analysis was employed to extract the live-load effect of strain from the total measurement as sensitive feature for structural health diagnosis. Finally the EWMA control chart was designed to complete the structural health diagnosis of bridge, in which the multi-stage control limits are estimated by reliability analysis. The results from application in a concrete cable-stayed bridge show significant effectiveness of the proposed method in health diagnosis of bridge.

Demerit-GWMA Control Chart for Demerit Statistics

2010 ◽  
Vol 156-157 ◽  
pp. 413-421
Author(s):  
Hae Woon Kang ◽  
Chang Wook Kang ◽  
Jae Won Baik ◽  
Sung Ho Nam
Keyword(s):  
Control Chart ◽  
Mobile Phones ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Process Mean ◽  
Average Technique ◽  
Complex Products ◽  
Ewma Control Chart ◽  
Weighted Moving Average ◽  
Exponentially Weighted

A classical Demerit control chart is used to monitor the process through which various types of defects in complex products, such as automobiles, computers, mobile phones, etc. are found in general. As a technique for rapidly detecting small shifts of the process mean in the control chart, the EWMA(exponentially weighted moving average) technique is very effective. This study suggested the Demerit-GWMA control chart, combining the GWMA(generally weighted moving average) technique, which shows better performance than EWMA technique in detecting small shifts of process mean, into the classical Demerit control chart, and evaluated its performance. Through the evaluation of its performance, it was found that the Demerit-GWMA control chart is more sensitive than both the classical Demerit control chart and the Demerit-EWMA control chart in detecting small shifts of process mean.

ZERO INFLATED POISSON EWMA CONTROL CHART FOR MONITORING RARE HEALTH-RELATED EVENTS

2012 ◽  
Vol 12 (04) ◽  
pp. 1250065 ◽  
Author(s):  
AMIR AFSHIN FATAHI ◽  
RASSOUL NOOROSSANA ◽  
PERSHANG DOKOUHAKI ◽  
BABAK FARHANG MOGHADDAM
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Random Variable ◽  
Performance Measure ◽  
Performance Criteria ◽  
Ewma Chart ◽  
Ewma Control Chart ◽  
Health Related ◽  
Run Lengths ◽  
Approach Zero

Recently, rare health events issue has motivated many researches in the field of control charting. Various methods such as g-type control chart, g-type CUSUM control chart, sets method, CUSCORE method, SHDA method and the Bernoulli CUSUM have been developed in this regard, in which each of them has a specific approach to the problem. As a relatively new approach, zero inflation in Poisson distribution, named ZIP distribution can be applied. In this paper, an exponentially weighted moving average (EWMA) control chart is developed for the ZIP random variable to monitor rare health-related events with a predefined performance measure value. Since the ZIP-EWMA plotted data are dependent, Markov chain approach is applied to calculate average run lengths (ARLs) as the control chart performance criteria. Based on the ARL measure, the ZIP-EWMA chart performs better in comparison with the methods available in the literature. As the main contribution of this paper is the development a control chart which performs better than the previously proposed charts. Also, a motivating real case study related to monitoring needle-stick rare occurrences in a hospital is investigated to show the applicability of the developed chart.

CV Control Chart Using GWMA Technique

2011 ◽  
Vol 337 ◽  
pp. 247-254 ◽  
Author(s):  
Eui Pyo Hong ◽  
Hae Woon Kang ◽  
Chang Wook Kang ◽  
Jae Won Baik
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Average Technique ◽  
Ewma Control Chart ◽  
Production Run ◽  
Average Control ◽  
Weighted Moving Average ◽  
Exponentially Weighted

When the production run is short and process parameters change 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, however, is not sensitive at small shifts in the magnitude of CV. This study suggest the CV-GWMA(generally weighted moving average) control chart, combining the GWMA technique, which shows better performance than the EWMA(exponentially weighted moving average) or DEWMA(double exponentially weighted moving average) technique in detecting small shifts of the process. Through a performance evaluation, the proposed control chart showed more excellent performance than the existing CV-EWMA control chart or the CV-DEWMA control chart in detecting small shifts in CV.

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