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Design optimization for the two-stage bivariate pattern recognition scheme

In manufacturing operations, unnatural process variation has become a major contributor to a poor quality product. Therefore, monitoring and diagnosis of variation is critical in quality control. Monitoring refers to the identification of process condition either it is running within in statisticall...

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Main Author: Mokhtar, Mohd Shukri
Format: Thesis
Language:English
English
English
Published: 2015
Subjects:
TS155-194 Production management. Operations management
Online Access:http://eprints.uthm.edu.my/1380/2/MOHD%20SHUKRI%20MOKHTAR%20COPYRIGHT%20DECLARATION.pdf
http://eprints.uthm.edu.my/1380/1/24p%20MOHD%20SHUKRI%20MOKHTAR.pdf
http://eprints.uthm.edu.my/1380/3/MOHD%20SHUKRI%20MOKHTAR%20WATERMARK.pdf
http://eprints.uthm.edu.my/1380/
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spelling my.uthm.eprints.13802021-10-03T06:37:00Z http://eprints.uthm.edu.my/1380/ Design optimization for the two-stage bivariate pattern recognition scheme Mokhtar, Mohd Shukri TS155-194 Production management. Operations management In manufacturing operations, unnatural process variation has become a major contributor to a poor quality product. Therefore, monitoring and diagnosis of variation is critical in quality control. Monitoring refers to the identification of process condition either it is running within in statistically in-control or out-of-control, whereas diagnosis refers to the identification of the source of out-of-control process. Selection of SPC scheme becomes more challenging when involving two correlated variables, which are known as bivariate quality control (BQC). Generally, the traditional SPC charting schemes were known to be effective in monitoring aspects, but there were unable to provide information towards diagnosis. In order to overcome this issue, many researches proposed an artificial neural network (ANN) - based pattern recognition schemes. Such schemes were mainly utilize raw data as input representation into an ANN recognizer, which resulted in limited performance. In this research, an integrated MEWMA-ANN scheme was investigated. The optimal design parameters for the MEWMA control chart have been studied. The study focused on BQC with variation in mean shifts (μ = ±0.75 ~ 3.00) standard deviations and cross correlation function (ρ = 0.1 ~ 0.9). The monitoring and diagnosis performances were evaluated based on the average run length (ARL0, ARL1) and recognition accuracy (RA) respectively. The selected optimal design parameters with λ=0.10, H=8.64 gave better performance among the other designs, namely, average run length, ARL1=3.24 ~ 16.93 (for out-of-control process) and recognition accuracy, RA=89.05 ~ 97.73%. For in-control process, design parameters with λ=0.40, H=10.31 parameter gave superior performance with ARL0 = 676.81 ~ 921.71, which is more effective in avoiding false alarm with any correlation. 2015-06 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/1380/2/MOHD%20SHUKRI%20MOKHTAR%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/1380/1/24p%20MOHD%20SHUKRI%20MOKHTAR.pdf text en http://eprints.uthm.edu.my/1380/3/MOHD%20SHUKRI%20MOKHTAR%20WATERMARK.pdf Mokhtar, Mohd Shukri (2015) Design optimization for the two-stage bivariate pattern recognition scheme. Masters thesis, Universiti Tun Hussein Onn Malaysia.
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
English
English
topic TS155-194 Production management. Operations management
spellingShingle TS155-194 Production management. Operations management
Mokhtar, Mohd Shukri
Design optimization for the two-stage bivariate pattern recognition scheme
description In manufacturing operations, unnatural process variation has become a major contributor to a poor quality product. Therefore, monitoring and diagnosis of variation is critical in quality control. Monitoring refers to the identification of process condition either it is running within in statistically in-control or out-of-control, whereas diagnosis refers to the identification of the source of out-of-control process. Selection of SPC scheme becomes more challenging when involving two correlated variables, which are known as bivariate quality control (BQC). Generally, the traditional SPC charting schemes were known to be effective in monitoring aspects, but there were unable to provide information towards diagnosis. In order to overcome this issue, many researches proposed an artificial neural network (ANN) - based pattern recognition schemes. Such schemes were mainly utilize raw data as input representation into an ANN recognizer, which resulted in limited performance. In this research, an integrated MEWMA-ANN scheme was investigated. The optimal design parameters for the MEWMA control chart have been studied. The study focused on BQC with variation in mean shifts (μ = ±0.75 ~ 3.00) standard deviations and cross correlation function (ρ = 0.1 ~ 0.9). The monitoring and diagnosis performances were evaluated based on the average run length (ARL0, ARL1) and recognition accuracy (RA) respectively. The selected optimal design parameters with λ=0.10, H=8.64 gave better performance among the other designs, namely, average run length, ARL1=3.24 ~ 16.93 (for out-of-control process) and recognition accuracy, RA=89.05 ~ 97.73%. For in-control process, design parameters with λ=0.40, H=10.31 parameter gave superior performance with ARL0 = 676.81 ~ 921.71, which is more effective in avoiding false alarm with any correlation.
format Thesis
author Mokhtar, Mohd Shukri
author_facet Mokhtar, Mohd Shukri
author_sort Mokhtar, Mohd Shukri
title Design optimization for the two-stage bivariate pattern recognition scheme
title_short Design optimization for the two-stage bivariate pattern recognition scheme
title_full Design optimization for the two-stage bivariate pattern recognition scheme
title_fullStr Design optimization for the two-stage bivariate pattern recognition scheme
title_full_unstemmed Design optimization for the two-stage bivariate pattern recognition scheme
title_sort design optimization for the two-stage bivariate pattern recognition scheme
publishDate 2015
url http://eprints.uthm.edu.my/1380/2/MOHD%20SHUKRI%20MOKHTAR%20COPYRIGHT%20DECLARATION.pdf
http://eprints.uthm.edu.my/1380/1/24p%20MOHD%20SHUKRI%20MOKHTAR.pdf
http://eprints.uthm.edu.my/1380/3/MOHD%20SHUKRI%20MOKHTAR%20WATERMARK.pdf
http://eprints.uthm.edu.my/1380/
_version_ 1738580852040794112
score 13.211869

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