A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts

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. Th...

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Main Author:	Masood, Ibrahim
Format:	Thesis
Language:	English
Published:	2012
Subjects:	TS Manufactures
Online Access:	http://eprints.uthm.edu.my/2539/1/24p%20IBRAHIM%20MASOOD.pdf
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id	my-uthm-ep.2539
record_format	uketd_dc
spelling	my-uthm-ep.25392021-11-01T02:34:53Z A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts 2012-01 Masood, Ibrahim TS Manufactures 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 to enable balanced monitoring and accurate diagnosis was investigated in order to improve such limitations. Design considerations involved extensive simulation experiments to select input representation based on raw data and statistical features, recognizer design structure based on individual and synergistic 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 ~ 0.9 and mean shift, μ = ± 0.75 ~ 3.00 standard deviations. Among the investigated designs, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network scheme gave superior performance, namely, average run lengths, ARL1 = 3.18 ~ 16.75 (for out-of-control process) and ARL0 = 452.13 (for in�control process), and recognition accuracy, RA = 89.5 ~ 98.5%. The proposed scheme was validated using an industrial case study from machining process of audio-video device component. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis of correlated process mean shifts 2012-01 Thesis http://eprints.uthm.edu.my/2539/ http://eprints.uthm.edu.my/2539/1/24p%20IBRAHIM%20MASOOD.pdf text en public phd doctoral Universiti Teknologi Malaysia Fakulti Kejuruteraan Mekanikal
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English
topic	TS Manufactures TS Manufactures
spellingShingle	TS Manufactures TS Manufactures Masood, Ibrahim A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts
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 to enable balanced monitoring and accurate diagnosis was investigated in order to improve such limitations. Design considerations involved extensive simulation experiments to select input representation based on raw data and statistical features, recognizer design structure based on individual and synergistic 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 ~ 0.9 and mean shift, μ = ± 0.75 ~ 3.00 standard deviations. Among the investigated designs, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network scheme gave superior performance, namely, average run lengths, ARL1 = 3.18 ~ 16.75 (for out-of-control process) and ARL0 = 452.13 (for in�control process), and recognition accuracy, RA = 89.5 ~ 98.5%. The proposed scheme was validated using an industrial case study from machining process of audio-video device component. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis of correlated process mean shifts
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Masood, Ibrahim
author_facet	Masood, Ibrahim
author_sort	Masood, Ibrahim
title	A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts
title_short	A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts
title_full	A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts
title_fullStr	A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts
title_full_unstemmed	A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts
title_sort	scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts
granting_institution	Universiti Teknologi Malaysia
granting_department	Fakulti Kejuruteraan Mekanikal
publishDate	2012
url	http://eprints.uthm.edu.my/2539/1/24p%20IBRAHIM%20MASOOD.pdf
_version_	1747830964160036864

A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts

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