scholarly journals MULTIVARIATE STATISTICAL PROCESS OF HOTELLING’S T2 CONTROL CHARTS PROCEDURES WITH INDUSTRIAL APPLICATION

2017 ◽  
Vol 18 (1) ◽  
pp. 1-44
Author(s):  
M. S. Hamed
Keyword(s):  
Industrial Application ◽  
Control Charts ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Hotelling's T2 ◽  
Hotelling’S T2

scholarly journals Multivariate Statistical Quality Control Based on Ranked Set Sampling

2017 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Hakan Eygü ◽  
M. Suphi Özçomak
Keyword(s):  
Control Charts ◽  
Sampling Methods ◽  
Average Run Length ◽  
Ranked Set Sampling ◽  
Multivariate Statistical ◽  
Run Length ◽  
Hotelling's T2 ◽  
Median Ranked Set Sampling ◽  
Hotelling’S T2 ◽  
T2 Control Chart

The sample of the study was formed using simple random sampling, ranked set sampling, extreme ranked set sampling and median ranked set sampling. At the end of this process, the researcher created Hotelling’s T2 control charts, a multivariate statistical process control method. The performances of SRS, RSS, ERSS and MRSS sampling methods were compared to one another using these control charts. A simulation was performed to see the average run-length values for Hotelling’s T2 control charts, and these findings were also used for the comparison of the sampling performances.At the end of the study, the researcher formed a sample using median ranked set sampling and created the Hotelling’s T2 control chart. As a result of this operation, the researcher found that there was an out-of-control signal in the process, while there was no such signal in other sampling methods. When the average run-length values obtained from Hotelling’s T2 control charts were compared, it was seen that a shift in the process was detected by the ranked set sampling earlier, when compared to other sampling methods. This paper it can be said that the methods used are unique to the literature because they are applied to multivariate data.

The efficiency of constructed bivariate copulas for MEWMA and Hotelling’s T2 control charts

2019 ◽  
pp. 1-15
Author(s):  
Sirasak Sasiwannapong ◽  
Saowanit Sukparungsee ◽  
Piyapatr Busababodhin ◽  
Yupaporn Areepong
Keyword(s):  
Control Charts ◽  
Hotelling's T2 ◽  
Hotelling’S T2

scholarly journals AN APPLICATION OF HOTELLING’S T2 TEST FOR THE COMPARISON OF THE VISUAL-ACOUSTIC METHOD IN THE IDENTIFICATION OF INGESTIVE CATTLE BEHAVIOR

2020 ◽  
Vol 38 (1) ◽  
pp. 79
Author(s):  
Valdemiro Piedade VIGAS ◽  
Denise VOLPI ◽  
Fabiana Villa ALVES ◽  
Giovana Oliveira SILVA ◽  
Erlandson Ferreira SARAIVA
Keyword(s):  
Test Performance ◽  
Production Systems ◽  
Acoustic Method ◽  
Visual Observation ◽  
Ingestive Behavior ◽  
Multivariate Statistical ◽  
Visual Method ◽  
Hotelling's T2 ◽  
Hotelling’S T2 ◽  
Hotelling’S T2 Test

The bioacoustic method is an important tool for the identification of the ingestive behavior of ruminants, especially in extensive production systems. This is mainly due to its potential to solve the deficiencies presented by the usual method, which is based on the visual observation of the animals. In this article, we present a study whose main objective is to evaluate the accuracy of the bioacoustic method over the visual method to record the macroactivity of grazing cattle ingestive behavior. The comparison of the methods is made in terms of a multivariate statistical approach based on the use of Hotelling’s T2 test. To verify the test performance in comparing the methods, we developed a simulation study using a resampling approach. The results show that the bioacoustic method can be an effective alternative to the visual method, with the advantage of being a noninvasive method that also allows the analysis of the micro events of ingestive behavior.

scholarly journals A Framework for Diagnosing the Out-of-Control Signals in Multivariate Process Using Optimized Support Vector Machines

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-fu Li ◽  
Sheng Hu ◽  
Zheng-yuan Wei ◽  
Zhi-qiang Liao
Keyword(s):  
Support Vector Machines ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Support Vector ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Control Signals ◽  
Vector Machines ◽  
Multivariate Control

Multivariate statistical process control is the continuation and development of unitary statistical process control. Most multivariate statistical quality control charts are usually used (in manufacturing and service industries) to determine whether a process is performing as intended or if there are some unnatural causes of variation upon an overall statistics. Once the control chart detects out-of-control signals, one difficulty encountered with multivariate control charts is the interpretation of an out-of-control signal. That is, we have to determine whether one or more or a combination of variables is responsible for the abnormal signal. A novel approach for diagnosing the out-of-control signals in the multivariate process is described in this paper. The proposed methodology uses the optimized support vector machines (support vector machine classification based on genetic algorithm) to recognize set of subclasses of multivariate abnormal patters, identify the responsible variable(s) on the occurrence of abnormal pattern. Multiple sets of experiments are used to verify this model. The performance of the proposed approach demonstrates that this model can accurately classify the source(s) of out-of-control signal and even outperforms the conventional multivariate control scheme.

Quality Control in Cocoa Powder Production Process: A Robust MSPC Approach

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
S. L. Lee ◽  
M. A. Djauhari
Keyword(s):  
Production Process ◽  
Control Charts ◽  
Masking Effect ◽  
Process Mean ◽  
Minimum Covariance Determinant ◽  
Cocoa Powder ◽  
Hotelling's T2 ◽  
Powder Production ◽  
Hotelling’S T2 ◽  
T2 Control Chart

To monitor a multivariate process mean, Hotelling’s T2 control chart is often used. However, the presence of multiple outliers may go undetected due to the masking effect or swamping effect. In this study, we propose a robust Hotelling’s T2 control charts where the mean vector and the covariance matrix are estimated by using fast minimum covariance determinant (FMCD) which gives a high breakdown point estimates. This study found that the latter approach performs far better than the former in terms of the ability in detecting an out-of-control situation during the start-up stage. We present and discuss our experience in monitoring the process mean of cocoa powder production process in a Malaysian company located in Johor Bahru.

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