Staff View: On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model

On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model

Advanced automatic data acquisition is now widely adopted in manufacturing industries and it is common to monitor several correlated quality variables simultaneously. Most of multivariate quality control charts are effective in detecting out-of-control signals based upon an overall statistics in mul...

Full description

Saved in:

Bibliographic Details
Main Authors:	Salehi, Mojtaba, Bahreininejad, Ardeshir, Nakhai, Isa
Format:	Article
Published:	Elsevier 2011
Subjects:	TJ Mechanical engineering and machinery TS Manufactures
Online Access:	http://eprints.um.edu.my/2058/ https://doi.org/10.1016/j.neucom.2010.12.020
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.um.eprints.2058
record_format	eprints
spelling	my.um.eprints.20582019-11-18T03:31:52Z http://eprints.um.edu.my/2058/ On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model Salehi, Mojtaba Bahreininejad, Ardeshir Nakhai, Isa TJ Mechanical engineering and machinery TS Manufactures Advanced automatic data acquisition is now widely adopted in manufacturing industries and it is common to monitor several correlated quality variables simultaneously. Most of multivariate quality control charts are effective in detecting out-of-control signals based upon an overall statistics in multivariate manufacturing processes. The main problem of such charts is that they can detect an out-of-control event but do not directly determine which variable or group of variables has caused the out-of-control signal and what is the magnitude of out of control. This study presents a hybrid learning-based model for on-line analysis of out-of-control signals in multivariate manufacturing processes. This model consists of two modules. In the first module using a support vector machine-classifier, type of unnatural pattern can be recognized. Then by using three neural networks for shift mean, trend and cycle it can be recognized magnitude of mean shift, slope of trend and cycle amplitude for each variable simultaneously in the second module. The performance of the proposed approach has been evaluated using two examples. The output generated by trained hybrid model is strongly correlated with the corresponding actual target value for each quality characteristic. The main contributions of this work are recognizing the type of unnatural pattern and classification major parameters for shift, trend and cycle and for each variable simultaneously by proposed hybrid model. (C) 2011 Elsevier B.V. All rights reserved. Elsevier 2011-06 Article PeerReviewed Salehi, Mojtaba and Bahreininejad, Ardeshir and Nakhai, Isa (2011) On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model. Neurocomputing, 74 (12-13). pp. 2083-2095. ISSN 0925-2312 https://doi.org/10.1016/j.neucom.2010.12.020 doi:10.1016/j.neucom.2010.12.020
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TJ Mechanical engineering and machinery TS Manufactures
spellingShingle	TJ Mechanical engineering and machinery TS Manufactures Salehi, Mojtaba Bahreininejad, Ardeshir Nakhai, Isa On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model
description	Advanced automatic data acquisition is now widely adopted in manufacturing industries and it is common to monitor several correlated quality variables simultaneously. Most of multivariate quality control charts are effective in detecting out-of-control signals based upon an overall statistics in multivariate manufacturing processes. The main problem of such charts is that they can detect an out-of-control event but do not directly determine which variable or group of variables has caused the out-of-control signal and what is the magnitude of out of control. This study presents a hybrid learning-based model for on-line analysis of out-of-control signals in multivariate manufacturing processes. This model consists of two modules. In the first module using a support vector machine-classifier, type of unnatural pattern can be recognized. Then by using three neural networks for shift mean, trend and cycle it can be recognized magnitude of mean shift, slope of trend and cycle amplitude for each variable simultaneously in the second module. The performance of the proposed approach has been evaluated using two examples. The output generated by trained hybrid model is strongly correlated with the corresponding actual target value for each quality characteristic. The main contributions of this work are recognizing the type of unnatural pattern and classification major parameters for shift, trend and cycle and for each variable simultaneously by proposed hybrid model. (C) 2011 Elsevier B.V. All rights reserved.
format	Article
author	Salehi, Mojtaba Bahreininejad, Ardeshir Nakhai, Isa
author_facet	Salehi, Mojtaba Bahreininejad, Ardeshir Nakhai, Isa
author_sort	Salehi, Mojtaba
title	On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model
title_short	On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model
title_full	On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model
title_fullStr	On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model
title_full_unstemmed	On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model
title_sort	on-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model
publisher	Elsevier
publishDate	2011
url	http://eprints.um.edu.my/2058/ https://doi.org/10.1016/j.neucom.2010.12.020
_version_	1651867303334117376
score	13.211869

On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model

Similar Items