Enhanced generally weighted moving average variance charts for monitoring process variance with individual observations

2019 ◽  
Vol 36 (1) ◽  
pp. 285-302 ◽  
Author(s):  
Chi‐Jui Huang ◽  
Shin‐Li Lu ◽  
Jen‐Hsiang Chen
Keyword(s):  
Moving Average ◽  
Monitoring Process ◽  
Average Variance ◽  
Process Variance ◽  
Weighted Moving Average

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

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.

scholarly journals Design of SN2-NEWMA Control Chart for Monitoring Process having Indeterminate Production Data

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 742 ◽  
Author(s):  
Aslam ◽  
Bantan ◽  
Khan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Control Chart ◽  
Moving Average ◽  
Logarithmic Transformation ◽  
Production Data ◽  
Exponentially Weighted Moving Average ◽  
Monitoring Process ◽  
Weighted Moving Average ◽  
Exponentially Weighted

The existing charts for monitoring the variance are designed under the assumption that all production data must consist of exact, precise, and determined observations. This paper presents the design of a new neutrosophic exponentially weighted moving average (NEWMA) combining with a neutrosophic logarithmic transformation chart for monitoring the variance having neutrosophic numbers. The computation of the neutrosophic control chart parameters is done through the neutrosophic Monte Carlo simulation (NMCS). The performance of the proposed chart is discussed with the existing charts.

scholarly journals New One-Sided EWMA t Charts without and with Variable Sampling Intervals for Monitoring the Process Mean

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yan Wang ◽  
Xuelong Hu ◽  
Xiaojian Zhou ◽  
Yulong Qiao ◽  
Shu Wu
Keyword(s):  
Moving Average ◽  
Simulation Method ◽  
Sampling Interval ◽  
Vital Role ◽  
Process Mean ◽  
Statistical Process ◽  
Variable Sampling ◽  
Sampling Intervals ◽  
Process Variance ◽  
Weighted Moving Average

In statistical process control (SPC), t charts play a vital role in the monitoring of the process mean, especially when the process variance is unknown. In this paper, two separate upper-sided and lower-sided exponentially weighted moving average (EWMA) t charts are first proposed and the Monte Carlo simulation method is used to obtain their run length (RL) properties. Compared with the traditional one-sided EWMA t charts and several run rules t charts, the proposed charts are proven to have better performance than these competing charts. In addition, by adding the variable sampling interval (VSI) feature to the proposed charts, the new VSI one-sided EWMA t charts are shown to detect different shift sizes in the process more efficient than the chart without VSI feature. Finally, an example of a milk filling process illustrates the use of the charts.

scholarly journals Efficient Homogeneously Weighted Moving Average Chart for Monitoring Process Mean Using an Auxiliary Variable

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 94021-94032 ◽  
Author(s):  
Nurudeen A. Adegoke ◽  
Adam N. H. Smith ◽  
Marti J. Anderson ◽  
Ridwan Adeyemi Sanusi ◽  
Matthew D. M. Pawley
Keyword(s):  
Moving Average ◽  
Auxiliary Variable ◽  
Process Mean ◽  
Monitoring Process ◽  
Weighted Moving Average
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