Optimization Design of 2-EWMA Control Chart Based on Random Process Shift

The exponentially weighted moving average (EWMA) control charts are widely used for detecting process shifts of small and moderate sizes in Statistical Process Control (SPC).This article presents an algorithm for the optimization design of a multi-EWMA scheme comprising two EWMA control charts (known as 2-EWMA chart) considering random process shifts in mean. The random process shifts in mean is characterized by a Rayleigh distribution. The design algorithm optimizes the charting parameters of the 2-EWMA chart based on loss function. Comparative study shows that the optimal 2-EWMA chart outperforms the original 2-EWMA chart, as well as the original EWMA chart. In general, this article will help to enhance the detection effectiveness of the 2-EWMA chart, and facilitate its applications in SPC.

Monitoring of mechanical sugarcane harvesting through control charts

Engenharia Agrícola ◽

10.1590/1809-4430-eng.agric.v35n6p1079-1092/2015 ◽

2015 ◽

Vol 35 (6) ◽

pp. 1079-1092 ◽

Cited By ~ 5

Author(s):

Murilo A. Voltarelli ◽

Rouverson P. da Silva ◽

Cristiano Zerbato ◽

Carla S. S. Paixão ◽

Tiago de O. Tavares

Keyword(s):

Process Control ◽

Control Chart ◽

Control Charts ◽

Moving Average ◽

Grinding Wheel ◽

Individual Values ◽

Statistical Process ◽

Minas Gerais State ◽

ABSTRACT Statistical process control in mechanized farming is a new way to assess operation quality. In this sense, we aimed to compare three statistical process control tools applied to losses in sugarcane mechanical harvesting to determine the best control chart template for this quality indicator. Losses were daily monitored in farms located within Triângulo Mineiro region, in Minas Gerais state, Brazil. They were carried over a period of 70 days in the 2014 harvest. At the end of the evaluation period, 194 samples were collected in total for each type of loss. The control charts used were individual values chart, moving average and exponentially weighted moving average. The quality indicators assessed during sugarcane harvest were the following loss types: full grinding wheel, stumps, fixed piece, whole cane, chips, loose piece and total losses. The control chart of individual values is the best option for monitoring losses in sugarcane mechanical harvesting, as it is of easier result interpretation, in comparison to the others.

Nonparametric EWMA-Type Control Charts for Monitoring Industrial Processes: An Overview

International Journal of Mathematical Engineering and Management Sciences ◽

10.33889/ijmems.2021.6.3.044 ◽

2021 ◽

Vol 6 (3) ◽

pp. 708-751

Author(s):

Ioannis S. Triantafyllou ◽

Mangey Ram

Keyword(s):

Probability Distribution ◽

Crucial Role ◽

Control Charts ◽

Moving Average ◽

Industrial Processes ◽

Statistical Process ◽

Practical Applications ◽

Memory Type ◽

In the present paper we provide an up-to-date overview of nonparametric Exponentially Weighted Moving Average (EWMA) control charts. Due to their nonparametric nature, such memory-type schemes are proved to be very useful for monitoring industrial processes, where the output cannot match to a particular probability distribution. Several fundamental contributions on the topic are mentioned, while recent advances are also presented in some detail. In addition, some practical applications of the nonparametric EWMA-type control charts are highlighted, in order to emphasize their crucial role in the contemporary online statistical process control.

CV Control Chart Using GWMA Technique

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.337.247 ◽

2011 ◽

Vol 337 ◽

pp. 247-254 ◽

Cited By ~ 4

Author(s):

Eui Pyo Hong ◽

Hae Woon Kang ◽

Chang Wook Kang ◽

Jae Won Baik

Keyword(s):

Control Chart ◽

Control Charts ◽

Moving Average ◽

Average Technique ◽

Ewma Control Chart ◽

Production Run ◽

Average Control ◽

Weighted Moving Average ◽

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.

Improved Hybrid Exponentially Weighted Moving Average Control Chart using Auxiliary Information

Journal of Reliability and Statistical Studies ◽

10.13052/jrss0974-8024.1316 ◽

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 ◽

Statistical Process ◽

Weighted Moving Average ◽

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.

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

Journal of Reliability and Statistical Studies ◽

10.13052/jrss2229-5666.12214 ◽

2019 ◽

pp. 187-198 ◽

Cited By ~ 1

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 ◽

Ewma Chart ◽

Weighted Moving Average ◽

Better Than ◽

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.

GRAFIK PENGENDALI MIXED EXPONENTIALLY WEIGHTED MOVING AVERAGE – CUMULATIVE SUM (MEC) DALAM ANALISIS PENGAWASAN PROSES PRODUKSI (Studi Kasus : Wingko Babat Cap “Moel”)

Jurnal Gaussian ◽

10.14710/j.gauss.v10i1.30938 ◽

2021 ◽

Vol 10 (1) ◽

pp. 114-124

Author(s):

Aulia Resti ◽

Tatik Widiharih ◽

Rukun Santoso

Keyword(s):

Control Chart ◽

Control Charts ◽

Average Run Length ◽

Moving Average ◽

Cumulative Sum ◽

Statistical Process ◽

Cusum Charts ◽

Weighted Moving Average ◽

Quality control is an important role in industry for maintain quality stability. Statistical process control can quickly investigate the occurrence of unforeseen causes or process shifts using control charts. Mixed Exponentially Weighted Moving Average - Cumulative Sum (MEC) control chart is a tool used to monitor and evaluate whether the production process is in control or not. The MEC control chart method is a combination of the Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts. Combining the two charts aims to increase the sensitivity of the control chart in detecting out of control. To compare the sensitivity level of the EWMA, CUSUM, and MEC methods, the Average Run Length (ARL) was used. From the comparison of ARL values, the MEC chart is the most sensitive control chart in detecting out of control compared to EWMA and CUSUM charts for small shifts. Keywords: Grafik Pengendali, Exponentially Weighted Moving Average, Cumulative Sum, Mixed EWMA-CUSUM, Average Run Lenght, EWMA, CUSUM, MEC, ARL

Time Series Analysis and Statistical Process Control for Anomaly Detection: An Application to the Situation of PM 10 in Northern Thailand

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.866.379 ◽

2017 ◽

Vol 866 ◽

pp. 379-382

Author(s):

Unchalee Tonggumnead ◽

Kittipong Klinjan

Keyword(s):

Process Control ◽

Moving Average ◽

Correlated Data ◽

Cusum Chart ◽

Statistical Process ◽

Shewhart Chart ◽

Ewma Chart ◽

Pm 10 ◽

The monitoring of processes is a vital mechanism for ensuring that such processes remain safe and under control. The present research aims to solve problems associated with correlated data by applying the Box-Jenkins method integrated with statistical process control (SPC) tools, namely the Shewhart chart, the moving average chart, the cumulative sum (CUSUM) chart, and the exponentially weighted moving-average (EWMA) chart. The efficiency of the four SPC tools was also compared in terms of the false alarm rate (FAR) and the missed detection rate (MDR). The findings indicated that the EWMA chart was the most effective in detecting anomaly, the Shewhart chart and the moving average chart produced high MDR, and the CUSUM chart suffered the highest FAR.

ZIB-EWMA CONTROL CHART FOR MONITORING RARE HEALTH EVENTS

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519411004125 ◽

2011 ◽

Vol 11 (04) ◽

pp. 881-895 ◽

Cited By ~ 7

Author(s):

RASSOUL NOOROSSANA ◽

AMIR AFSHIN FATAHI ◽

PERSHANG DOKOUHAKI ◽

MASSOUD BABAKHANI

Keyword(s):

Control Chart ◽

Control Charts ◽

Average Run Length ◽

Moving Average ◽

Cusum Chart ◽

Health Events ◽

Ewma Chart ◽

Ewma Control Chart ◽

Significant Public Health ◽

Health Related

Monitoring rare health events, as a significant public health subject, has been considered recently by different authors. In this regard, different statistical methods such as g-type control chart, Poisson CUSUM control chart, sets-based methods, and Bernoulli CUSUM chart have been developed. Zero-inflated binomial (ZIB) distribution, due to its structure, can also be considered to develop methods for monitoring rare health-related events. If zero inflation is considered in the sampling data, and the sampling subgroup size is mandatory greater than 1, then the data best fits the ZIB distribution and the aforementioned control charts cannot be applied. ZIB distribution assumes that random shocks, corresponding to rare health events, occur and then number of failures in each subgroup fits a binomial distribution. In this paper, an exponentially weighted moving average (EWMA) control chart is applied for ZIB data to develop a ZIB-EWMA chart. Since ZIB-EWMA statistic values are not independent, Markov chain approach is considered to evaluate the performance of the proposed control chart in terms of average run length (ARL). According to the ARL measure, this ZIB-EWMA chart has a better performance in comparison with the methods available in the literature. In addition, a real case study related to rare infections in a hospital is investigated to show the applicability of the proposed control chart.

Enhanced nonparametric control charts under simple and ranked set sampling schemes

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220931977 ◽

2020 ◽

Vol 42 (14) ◽

pp. 2744-2759 ◽

Cited By ~ 5

Author(s):

Zameer Abbas ◽

Hafiz Zafar Nazir ◽

Muhammad Abid ◽

Noureen Akhtar ◽

Muhammad Riaz

Keyword(s):

Control Charts ◽

Moving Average ◽

Statistical Process ◽

Sampling Schemes ◽

Signed Rank ◽

Process Location ◽

Weighted Moving Average ◽

Investigation and removal of unnatural variation in the processes of manufacturing, production and services require application of statistical process control. Control charts are the most famous and commonly used statistical process control tools to trace changes in the manufacturing and nonmanufacturing processes parameter(s). The nonparametric control charts become necessary when the distribution of underlying process is unknown or questionable. The nonparametric charts are robust alternative along with holding property of quick shift detection ability in process parameter(s). In this article, we have proposed nonparametric double exponentially weighted moving average chart based on Wilcoxon signed rank test under simple and ranked set sampling schemes for efficient monitoring of the process location. The proposed control charts are compared with classical exponentially weighted moving average, double exponentially weighted moving average, nonparametric exponentially weighted moving average sign, nonparametric exponentially weighted moving average signed rank, nonparametric cumulative sum signed rank charts using average run length and some other characteristics of run length distribution as performance measures. Comparison reveals that the proposed control charts performs better to detect all kinds of shifts in the process location than existing counterparts. A real-life application related to manufacturing process (the variable of interest is the diameter of piston ring) is also provided for the practical implementation of the proposed chart.

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

Jambura Journal of Mathematics ◽

10.34312/jjom.v4i1.11993 ◽

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 ◽

Ewma Control Chart ◽

Control Limits ◽

Weighted Moving Average ◽

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.