scholarly journals Penerapan Peta Kendali Neutrosophic Exponentially Weighted Moving Average (NEWMA) X dalam Monitoring Rata-Rata Proses Ketebalan Kaca

2022 ◽  
Vol 4 (1) ◽  
Author(s):  
Wibawati Wibawati ◽  
Widya Amalia Rahma ◽  
Muhammad Ahsan ◽  
Wilda Melia Udiatami
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Quality Characteristic ◽  
Average Process ◽  
Exponentially Weighted Moving Average ◽  
Ewma Control Chart ◽  
Control Limits ◽  
Weighted Moving Average ◽  
Exponentially Weighted

In the industrial sector, the measurement results of a quality characteristic often involve an uncertainty interval (interval indeterminacy). This causes the classical control chart to be less suitable for monitoring quality. Currently, a control chart with a neutrosophic approach has been developed. The neutrosophic control chart was developed based on the concept of neutrosophic numbers with control charts. One of the control charts that have been developed to monitor the mean process is the Neutrosophic Exponentially Weighted Moving Average (NEWMA) X control chart. This control chart is a combination of neutrosophic with classical EWMA control chart.  The neutrosophic control chart consists of two control charts, namely lower and upper, each of which consists of upper and lower control limits. Therefore, NEWMA X is more sensitive to detect out-of-control observations. In this research, the NEWMA X control chart will be used to monitor the average process of the thickness of the panasap dark grey 5mm glass produced by a glass industry. Through the analysis in this research, it was found that by using weighting λN [0, 10; 0, 10] and constant value kN [2, 565; 2, 675], the average process of the thickness of panasap dark grey 5mm glass has not beet controlled statistically because 21 observations were identified that were outside the control limits (out of control). When compared with the classical EWMA control chart with the same weighting λ, 17 observations were detected out of control. This proves that the NEWMA X control chart is more sensitive in detecting observations that are out of control because the determination of the in-control state is based on two values, lower and upper, both at the lower and upper control limits.

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.

On control chart for monitoring exponentially distributed quality characteristic

2019 ◽  
Vol 42 (2) ◽  
pp. 295-305 ◽  
Author(s):  
Olatunde A Adeoti
Keyword(s):  
Exponential Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Real Life ◽  
Quality Characteristic ◽  
Exponentially Weighted Moving Average ◽  
Run Lengths ◽  
Weighted Moving Average ◽  
Exponentially Weighted

The double exponentially weighted moving average (DEWMA) control chart has been observed to be more sensitive than the exponentially weighted moving average (EWMA) control chart for process monitoring assuming that the quality characteristic follows the normal distribution. In this paper, the DEWMA control chart is proposed for monitoring quality characteristics that follow the exponential distribution using variable transformation technique. The in-control and out-of-control average run lengths (ARLs) of the proposed control chart is obtained for equal and unequal smoothing constants. The performance of the proposed control chart with equal and unequal smoothing constants was investigated and compared with recent existing control charts in terms of the out-of-control average run lengths. Real life example is given to demonstrate the application of the proposed chart. The findings show that the performance of the proposed control chart outweighs existing control charts in the monitoring of process parameter when the quality variable follows exponential distribution for all shift sizes.

DEWMA Control Chart for the Coefficient of Variation

2011 ◽  
Vol 201-203 ◽  
pp. 1682-1688 ◽  
Author(s):  
Eui Pyo Hong ◽  
Hae Woon Kang ◽  
Chang Wook Kang
Keyword(s):  
Control Chart ◽  
Coefficient Of Variation ◽  
Control Charts ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Average Technique ◽  
Small Shift ◽  
Production Run ◽  
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 shift in the magnitude of CV. The CV-EWMA (exponentially weighted moving average) control chart which was developed recently is effective in detecting a small shifts of CV. In this paper, we propose the CV-DEWMA control chart, combining the DEWMA (double exponentially weighted moving average) technique. We show that CV-DEWMA control chart perform better than CV-EWMA control chart in detecting small shifts when sample size n is larger than 5.

scholarly journals EWMA Algorithm in Network Practice

2010 ◽  
Vol 5 (2) ◽  
pp. 160 ◽  
Author(s):  
Petar Cisar ◽  
Saša Bošnjak ◽  
Sanja Maravic Cisar
Keyword(s):  
Intrusion Detection ◽  
Computer Networks ◽  
Control Charts ◽  
Moving Average ◽  
Optimization Method ◽  
Exponentially Weighted Moving Average ◽  
Network Systems ◽  
Control Limits ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Intrusion detection is used to monitor and capture intrusions into computer and network systems which attempt to compromise their security. Many intrusions manifest in changes in the intensity of events occuring in computer networks. Because of the ability of exponentially weighted moving average (EWMA) control charts to monitor the rate of occurrences of events based on their intensity, this technique is appropriate for implementation in control limits based algorithms. The paper also gives a review of a possible optimization method. The validation check of results will be performed on authentic network samples.

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.

scholarly journals Improved Hybrid Exponentially Weighted Moving Average Control Chart using Auxiliary Information

2020 ◽  
Author(s):  
Sadia Tariq ◽  
Muhammad Noor-ul-Amin ◽  
Muhammad Hanif ◽  
Chi-Hyuck Jun 
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Real Life ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Exponentially Weighted Moving Average ◽  
Statistical Process ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Statistical process control is an important tool for maintaining the quality of a production process. Several control charts are available to monitor changes in process parameters. In this study, a control chart for the process mean is proposed. For this purpose, an auxiliary variable is used in the form of a regression estimator under the configuration of the hybrid exponentially weighted moving average (HEWMA) control chart. The proposed chart is evaluated by conducting a simulation study. The results showed that the proposed chart is sensitive with respect to the HEWMA chart. A real-life application is also presented to demonstrate the performance of the proposed control chart.

scholarly journals HYBRID EXPONENTIALLY WEIGHTED MOVING AVERAGE (HEWMA) CONTROL CHART BASED ON EXPONENTIAL TYPE ESTIMATOR OF MEAN

2019 ◽  
pp. 187-198 ◽  
Author(s):  
Syed Muhammad Muslim Raza ◽  
Maqbool Hussain Sial ◽  
Muhammad Haider ◽  
Muhammad Moeen Butt
Keyword(s):  
Control Chart ◽  
Exponential Type ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Ewma Chart ◽  
Weighted Moving Average ◽  
Better Than ◽  
Exponentially Weighted

In this paper, we have proposed a Hybrid Exponentially Weighted Moving Average (HEWMA) control chart. The proposed control chart is based on the exponential type estimator for mean using two auxiliary variables (cf. Noor-ul-Amin and Hanif, 2012). We call it an EHEWMA control chart because it is based on the exponential estimator of the mean. From this study, the fact is revealed that E-HEWMA control chart shows more efficient results as compared to traditional/simple EWMA chart and DS.EWMA control chart (cf. Raza and Butt, 2018). The comparison of the E-HEWMA control chart is also performed with the DS-EWMA chart. The proposed chart also outperforms the other control chartsin comparison. The E-HEWMA chart can be used for efficient monitoring of the production process in manufacturing industries.A simulated example has been used to compare the proposed and traditional/simple EWMA charts and DS.EWMA control chart. The control charts' performance is measured using the average run length-out of control (ARL1). It is observed that the proposed chart performs better than existing EWMA control charts.  

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