Intergration of control chart and pattern recognizer for bivariate quality control

Monitoring and diagnosis of mean shifts in manufacturing processes become more challenging when involving two or more correlated variables. Unfortunately, most of the existing multivariate statistical process control schemes are only effective in rapid detection but suffer high false alarm. This is...

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Main Author:	Mohd Sohaimi, Nurul Adlihisam
Format:	Thesis
Language:	English English English
Published:	2015
Subjects:	TS155-194 Production management Operations management
Online Access:	http://eprints.uthm.edu.my/1413/2/NURUL%20ADLIHISAM%20MOHD%20SOHAIMI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1413/1/24p%20NURUL%20ADLIHISAM%20MOHD%20SOHAIMI.pdf http://eprints.uthm.edu.my/1413/3/NURUL%20ADLIHISAM%20MOHD%20SOHAIMI%20WATERMARK.pdf
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spelling	my-uthm-ep.14132021-10-03T06:46:26Z Intergration of control chart and pattern recognizer for bivariate quality control 2015-06 Mohd Sohaimi, Nurul Adlihisam TS155-194 Production management. Operations management Monitoring and diagnosis of mean shifts in manufacturing processes become more challenging when involving two or more correlated variables. Unfortunately, most of the existing multivariate statistical process control schemes are only effective in rapid detection but suffer high false alarm. This is referred to as imbalanced performance monitoring. The problem becomes more complicated when dealing with small mean shift particularly in identifying the causable variables. In this research, a scheme that integrated the control charting and pattern recognition technique has been investigated toward improving the quality control (QC) performance. Design considerations involved extensive simulation experiments to select input representation based on raw data and statistical features, recognizer design structure based on individual and Statistical Features-ANN models, and monitoring-diagnosis approach based on single stage and two stages techniques. The study focuses on correlated process mean shifts for cross correlation function, ρ = 0.1, 0.5, 0.9, and mean shift, μ = ± 0.75 ~ 3.00 standard deviations. Among the investigated design, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network scheme provides superior performance, namely the Average Run Length for grand average ARL1 = 7.55 7.78 ( for out-of-control) and ARL0 = 491.03 (small mean shift) and 524.80 (large mean shift) in control process and the grand average for recognition accuracy (RA) = 96.36 98.74. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis of correlated process mean shifts. 2015-06 Thesis http://eprints.uthm.edu.my/1413/ http://eprints.uthm.edu.my/1413/2/NURUL%20ADLIHISAM%20MOHD%20SOHAIMI%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1413/1/24p%20NURUL%20ADLIHISAM%20MOHD%20SOHAIMI.pdf text en public http://eprints.uthm.edu.my/1413/3/NURUL%20ADLIHISAM%20MOHD%20SOHAIMI%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Mechanical and Manufacturing Engineering
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TS155-194 Production management Operations management
spellingShingle	TS155-194 Production management Operations management Mohd Sohaimi, Nurul Adlihisam Intergration of control chart and pattern recognizer for bivariate quality control
description	Monitoring and diagnosis of mean shifts in manufacturing processes become more challenging when involving two or more correlated variables. Unfortunately, most of the existing multivariate statistical process control schemes are only effective in rapid detection but suffer high false alarm. This is referred to as imbalanced performance monitoring. The problem becomes more complicated when dealing with small mean shift particularly in identifying the causable variables. In this research, a scheme that integrated the control charting and pattern recognition technique has been investigated toward improving the quality control (QC) performance. Design considerations involved extensive simulation experiments to select input representation based on raw data and statistical features, recognizer design structure based on individual and Statistical Features-ANN models, and monitoring-diagnosis approach based on single stage and two stages techniques. The study focuses on correlated process mean shifts for cross correlation function, ρ = 0.1, 0.5, 0.9, and mean shift, μ = ± 0.75 ~ 3.00 standard deviations. Among the investigated design, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network scheme provides superior performance, namely the Average Run Length for grand average ARL1 = 7.55 7.78 ( for out-of-control) and ARL0 = 491.03 (small mean shift) and 524.80 (large mean shift) in control process and the grand average for recognition accuracy (RA) = 96.36 98.74. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis of correlated process mean shifts.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohd Sohaimi, Nurul Adlihisam
author_facet	Mohd Sohaimi, Nurul Adlihisam
author_sort	Mohd Sohaimi, Nurul Adlihisam
title	Intergration of control chart and pattern recognizer for bivariate quality control
title_short	Intergration of control chart and pattern recognizer for bivariate quality control
title_full	Intergration of control chart and pattern recognizer for bivariate quality control
title_fullStr	Intergration of control chart and pattern recognizer for bivariate quality control
title_full_unstemmed	Intergration of control chart and pattern recognizer for bivariate quality control
title_sort	intergration of control chart and pattern recognizer for bivariate quality control
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Mechanical and Manufacturing Engineering
publishDate	2015
url	http://eprints.uthm.edu.my/1413/2/NURUL%20ADLIHISAM%20MOHD%20SOHAIMI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1413/1/24p%20NURUL%20ADLIHISAM%20MOHD%20SOHAIMI.pdf http://eprints.uthm.edu.my/1413/3/NURUL%20ADLIHISAM%20MOHD%20SOHAIMI%20WATERMARK.pdf
_version_	1747830783702204416

Intergration of control chart and pattern recognizer for bivariate quality control

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