scholarly journals A Control Chart Based on Moving Average Model Functioned for Poisson Distribution

2020 ◽  
Vol 3 (10) ◽  
Author(s):  
Hira Arooj ◽  
◽  
Khawar Iqbal Malik ◽  
Keyword(s):  
Poisson Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Current Control ◽  
Average Model ◽  
Time Intervals ◽  
Manufacture Process ◽  
Moving Average Model

A control chart used with MA control chart to track the number of faulty products or faults suggested. When the characteristics of quality of interest obey a Poisson distribution. A specified number of objects are observed at various time intervals in order to observe the number of non-conformities. By measuring ARLs under different sample sizes and parameters by considering ARLs in power, the output of the proposed chart is evaluated. It should be noted The proposed control chart seems to be morereliable than the traditional current control charts in detecting small adjustments in the manufacture process.

A hybrid exponentially weighted moving average chart for COM-Poisson distribution

2016 ◽  
Vol 40 (2) ◽  
pp. 456-461 ◽  
Author(s):  
Muhammad Aslam ◽  
Nasrullah Khan ◽  
Chi-Hyuck Jun
Keyword(s):  
New York ◽  
Poisson Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Statistical Quality Control ◽  
Exponentially Weighted Moving Average ◽  
Run Lengths ◽  
Weighted Moving Average ◽  
Exponentially Weighted

We provide the complete design of a hybrid exponentially weighted moving average (HEWMA) control chart for COM-Poisson distribution. The necessary measures of the proposed control chart are given in this manuscript, and the average run lengths (ARLs) are determined through Monte Carlo simulation for various values of specified parameters. The performance of the proposed chart is compared with two existing control charts. The proposed chart is more efficient than these two existing charts in terms of ARLs; application of the proposed chart is described with the help of Montgomery’s data ( Introduction to Statistical Quality Control, John Wiley & Sons, New York, 2007).

Improving Phase I Monitoring of Dirichlet Regression Profiles

2018 ◽  
Vol 25 (06) ◽  
pp. 1850030 ◽  
Author(s):  
Hourieh Foroutan ◽  
Amirhossein Amiri ◽  
Reza Kamranrad
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Control Charts ◽  
Compositional Data ◽  
Ratio Test ◽  
Statistical Process ◽  
Explanatory Variables ◽  
Monitoring Procedure ◽  
Multivariate Control

In most statistical process control (SPC) applications, quality of a process or product is monitored by univariate or multivariate control charts. However, sometimes a functional relationship between a response variable and one or more explanatory variables is established and monitored over time. This relationship is called “profile” in SPC literature. In this paper, we specifically consider processes with compositional data responses, including multivariate positive observations summing to one. The relationship between compositional data responses and explanatory variables is modeled by a Dirichlet regression profile. We develop a monitoring procedure based on likelihood ratio test (lrt) for Phase I monitoring of Dirichlet regression profiles. Then, we compare the performance of the proposed method with the best method in the literature in terms of probability of signal. The results of simulation studies show that the proposed control chart has better performance in Phase I monitoring than the competing control chart. Moreover, the proposed method is able to estimate the real time of a change as well. The performance of this feature is also investigated through simulation runs which show the satisfactory performance. Finally, the application of the proposed method is illustrated based on a real case in comparison with the existing method.

scholarly journals Monitoring of mechanical sugarcane harvesting through control charts

2015 ◽  
Vol 35 (6) ◽  
pp. 1079-1092 ◽  
Author(s):  
Murilo A. Voltarelli ◽  
Rouverson P. da Silva ◽  
Cristiano Zerbato ◽  
Carla S. S. Paixão ◽  
Tiago de O. Tavares
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Grinding Wheel ◽  
Individual Values ◽  
Statistical Process ◽  
Minas Gerais State ◽  
Weighted Moving Average

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.

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