Identification of shift variation in bivariate process using pattern recognition technique

In quality control, the identification of unnatural variation in mean shifts is a challenge when dealing with two correlated quality characteristics (bivariate). Various schemes based on statistical process control pattern recognition (SPCPR) approach have been proposed to monitor the presence of un...

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Main Author:	Mohd Haizan, Mohamad Azrul Azhad
Format:	Thesis
Language:	English English English
Published:	2019
Subjects:	TS155-194 Production management Operations management
Online Access:	http://eprints.uthm.edu.my/560/1/24p%20MOHAMAD%20AZRUL%20AZHAD%20MOHD%20HAIZAN.pdf http://eprints.uthm.edu.my/560/2/MOHAMAD%20AZRUL%20AZHAD%20MOHD%20HAIZAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/560/3/MOHAMAD%20AZRUL%20AZHAD%20MOHD%20HAIZAN%20WATERMARK.pdf
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spelling	my-uthm-ep.5602021-08-05T03:25:29Z Identification of shift variation in bivariate process using pattern recognition technique 2019-01 Mohd Haizan, Mohamad Azrul Azhad TS155-194 Production management. Operations management In quality control, the identification of unnatural variation in mean shifts is a challenge when dealing with two correlated quality characteristics (bivariate). Various schemes based on statistical process control pattern recognition (SPCPR) approach have been proposed to monitor the presence of unnatural variation and diagnose its sources. However, existing studies have focused either on limited sources of unnatural variation and/or lack in avoiding false alarm. In terms for unnatural variation, the classification of reciprocating patterns such as upward-downward and downward-upward shifts was less reported. In order to enhance its capability, this study aims at proposing an improved design of SPCPR scheme for enabling classification of nine bivariate patterns with high accuracy and reduced false alarm. There were two schemes investigated in this study: (i) Statistical Features-Multilayer Perceptron (SF-MLP) and (ii) Integrated Multivariate Exponentially Weighted Moving Average-Multilayer Perceptron (MEWMA-MLP). Input representation for the MLP recogniser in both schemes was designed based on summary statistical features and it was systematically selected using the design of experiments analysis. The average run lengths (ARL0, ARL1) and recognition accuracy percentage (RA) were used as the performance measures. In monitoring performance, the MEWMA-MLP provided a longer run of ARL0 (386.8 ~ 606.9) and ARL1 (2.76 ~ 9.63) compared to the SF-MLP (ARL0 = 160.8 ~529.3, ARL1 = 2.25 ~ 8.91). This result suggests that the MEWMA-MLP is better in avoiding false alarm, which exceeded the de facto level (ARL0 = 370) but requires a slightly longer run to detect the unnatural variation. In diagnosis performance, the MEWMA-MLP gave an improved range of recognition accuracy (RA= 86.5 ~ 99.3 %) compared to the SF-MLP (RA= 83.3 ~ 97.7 %) in classifying the sources of unnatural variation. Overall, this study opens a new perspective to enhance the capability of SPCPR scheme for the application of bivariate quality control. 2019-01 Thesis http://eprints.uthm.edu.my/560/ http://eprints.uthm.edu.my/560/1/24p%20MOHAMAD%20AZRUL%20AZHAD%20MOHD%20HAIZAN.pdf text en public http://eprints.uthm.edu.my/560/2/MOHAMAD%20AZRUL%20AZHAD%20MOHD%20HAIZAN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/560/3/MOHAMAD%20AZRUL%20AZHAD%20MOHD%20HAIZAN%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Mekanikal dan Pembuatan
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 Haizan, Mohamad Azrul Azhad Identification of shift variation in bivariate process using pattern recognition technique
description	In quality control, the identification of unnatural variation in mean shifts is a challenge when dealing with two correlated quality characteristics (bivariate). Various schemes based on statistical process control pattern recognition (SPCPR) approach have been proposed to monitor the presence of unnatural variation and diagnose its sources. However, existing studies have focused either on limited sources of unnatural variation and/or lack in avoiding false alarm. In terms for unnatural variation, the classification of reciprocating patterns such as upward-downward and downward-upward shifts was less reported. In order to enhance its capability, this study aims at proposing an improved design of SPCPR scheme for enabling classification of nine bivariate patterns with high accuracy and reduced false alarm. There were two schemes investigated in this study: (i) Statistical Features-Multilayer Perceptron (SF-MLP) and (ii) Integrated Multivariate Exponentially Weighted Moving Average-Multilayer Perceptron (MEWMA-MLP). Input representation for the MLP recogniser in both schemes was designed based on summary statistical features and it was systematically selected using the design of experiments analysis. The average run lengths (ARL0, ARL1) and recognition accuracy percentage (RA) were used as the performance measures. In monitoring performance, the MEWMA-MLP provided a longer run of ARL0 (386.8 ~ 606.9) and ARL1 (2.76 ~ 9.63) compared to the SF-MLP (ARL0 = 160.8 ~529.3, ARL1 = 2.25 ~ 8.91). This result suggests that the MEWMA-MLP is better in avoiding false alarm, which exceeded the de facto level (ARL0 = 370) but requires a slightly longer run to detect the unnatural variation. In diagnosis performance, the MEWMA-MLP gave an improved range of recognition accuracy (RA= 86.5 ~ 99.3 %) compared to the SF-MLP (RA= 83.3 ~ 97.7 %) in classifying the sources of unnatural variation. Overall, this study opens a new perspective to enhance the capability of SPCPR scheme for the application of bivariate quality control.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohd Haizan, Mohamad Azrul Azhad
author_facet	Mohd Haizan, Mohamad Azrul Azhad
author_sort	Mohd Haizan, Mohamad Azrul Azhad
title	Identification of shift variation in bivariate process using pattern recognition technique
title_short	Identification of shift variation in bivariate process using pattern recognition technique
title_full	Identification of shift variation in bivariate process using pattern recognition technique
title_fullStr	Identification of shift variation in bivariate process using pattern recognition technique
title_full_unstemmed	Identification of shift variation in bivariate process using pattern recognition technique
title_sort	identification of shift variation in bivariate process using pattern recognition technique
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Mekanikal dan Pembuatan
publishDate	2019
url	http://eprints.uthm.edu.my/560/1/24p%20MOHAMAD%20AZRUL%20AZHAD%20MOHD%20HAIZAN.pdf http://eprints.uthm.edu.my/560/2/MOHAMAD%20AZRUL%20AZHAD%20MOHD%20HAIZAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/560/3/MOHAMAD%20AZRUL%20AZHAD%20MOHD%20HAIZAN%20WATERMARK.pdf
_version_	1747830639022833664

Identification of shift variation in bivariate process using pattern recognition technique

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