Some Omnibus Exponentially Weighted Moving Average Statistical Process Monitoring Schemes

Technometrics ◽  
1991 ◽  
Vol 33 (3) ◽  
pp. 299-313 ◽  
Author(s):  
Rickie Domangue ◽  
Steven C. Patch
Keyword(s):  
Process Monitoring ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Statistical Process ◽  
Statistical Process Monitoring ◽  
Weighted Moving Average ◽  
Exponentially Weighted

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

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 ◽  
Exponentially Weighted Moving Average ◽  
Cumulative Sum ◽  
Statistical Process ◽  
Cusum Charts ◽  
Weighted Moving Average ◽  
Exponentially Weighted

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

Enhanced nonparametric control charts under simple and ranked set sampling schemes

2020 ◽  
Vol 42 (14) ◽  
pp. 2744-2759 ◽  
Author(s):  
Zameer Abbas ◽  
Hafiz Zafar Nazir ◽  
Muhammad Abid ◽  
Noureen Akhtar ◽  
Muhammad Riaz
Keyword(s):  
Statistical Process Control ◽  
Control Charts ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Statistical Process ◽  
Sampling Schemes ◽  
Signed Rank ◽  
Process Location ◽  
Weighted Moving Average ◽  
Exponentially Weighted

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.

Statistical Process Control Based on Kalman Filter in Manufacturing Process

2011 ◽  
Vol 201-203 ◽  
pp. 986-989
Author(s):  
Pei Wang ◽  
Ding Hua Zhang ◽  
Shan Li ◽  
Ming Wei Wang ◽  
Bing Chen
Keyword(s):  
Kalman Filter ◽  
Process Control ◽  
Statistical Process Control ◽  
Manufacturing Process ◽  
Average Method ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Statistical Process ◽  
Weighted Moving Average ◽  
Exponentially Weighted

In order to reduce the impact of data noise to quality control and make monitor results more precise in manufacturing process, the method of statistical process control based on Kalman filter was proposed. In this method, the statistical process control model based on Kalman filter was built and the quality control method of exponentially weighted moving average based on Kalman filter was put forward. While monitoring manufacturing process, first the technology of Kalman filter was used to smooth data and to reduce noise, and then control charts were built by the method of exponentially weighted moving average to monitor quality. Finally, the performance of the exponentially weighted moving average method based on Kalman filter and the tranditional exponentially weighted moving average method was compared. The performance result illustrates the feasibility and validity of the proposed quality monitor method.

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