Rebooting kernel CCA method for nonlinear quality-relevant fault detection in process industries

Cheng, H; Liu, Y; Huang, D; Cai, B; Wang, Q

Rebooting kernel CCA method for nonlinear quality-relevant fault detection in process industries

Cheng, H Liu, Y Huang, D Cai, B Wang, Q

Permalink

Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Process Safety and Environmental Protection, 2021, 149, pp. 619-630
Issue Date:: 2021-05-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 1 May 2023

Adobe PDF

Download Accepted versionAdobe PDF (1.07 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Cheng, H
dc.contributor.author	Liu, Y
dc.contributor.author	Huang, D
dc.contributor.author	Cai, B
dc.contributor.author	Wang, Q https://orcid.org/0000-0002-5744-2331
dc.date.accessioned	2022-04-13T03:50:54Z
dc.date.available	2022-04-13T03:50:54Z
dc.date.issued	2021-05-01
dc.identifier.citation	Process Safety and Environmental Protection, 2021, 149, pp. 619-630
dc.identifier.issn	0957-5820
dc.identifier.issn	1744-3598
dc.identifier.uri	http://hdl.handle.net/10453/156192
dc.description.abstract	Process monitoring is essential and important strategy for ensuring process safety and product quality. However, due to the nonlinear characteristics and multiple working conditions in process industries, the traditional process monitoring method cannot be effectively applied. Therefore, we propose a novel process monitoring framework, termed as mixture enhanced kernel canonical correlation analysis framework (M-NAKCCA). The innovations and advantages of M-NAKCCA are as follows: 1). The traditional CCA method is re-boosted as a new method, M-NAKCCA, to better nonlinear fault detection. Also, a matter-element model (MEm) is assimilated into M-NAKCCA to make the information more refined. 2). To overcome the curse of dimensionality that usually occurs in the high-dimensional dataset, M-NAKCCA uses the Nyström approximation technology to compress the kernel matrix. Moreover, the T2 control chart is reconstructed and the corresponding control upper limit is re-configured to improve the method sensitivity and to better the fault detection performance. 3). The proposed M-NAKCCA framework is firstly used to monitor a wastewater treatment plant (WWTP) and chemical plant with diverse process behaviors. The experimental results showed that the M-NAKCCA framework achieved the best performance for both of case studies.
dc.language	English
dc.publisher	Elsevier
dc.relation	http://purl.org/au-research/grants/arc/DP200100933
dc.relation.ispartof	Process Safety and Environmental Protection
dc.relation.isbasedon	10.1016/j.psep.2021.03.025
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0102 Applied Mathematics, 0904 Chemical Engineering, 0911 Maritime Engineering, 0914 Resources Engineering and Extractive Metallurgy
dc.subject.classification	Chemical Engineering
dc.subject.classification	Strategic, Defence & Security Studies
dc.title	Rebooting kernel CCA method for nonlinear quality-relevant fault detection in process industries
dc.type	Journal Article
utslib.citation.volume	149
utslib.for	0102 Applied Mathematics
utslib.for	0904 Chemical Engineering
utslib.for	0911 Maritime Engineering
utslib.for	0914 Resources Engineering and Extractive Metallurgy
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2023-05-01T00:00:00+1000Z
dc.date.updated	2022-04-13T03:50:52Z
pubs.publication-status	Published
pubs.volume	149

Abstract:

Process monitoring is essential and important strategy for ensuring process safety and product quality. However, due to the nonlinear characteristics and multiple working conditions in process industries, the traditional process monitoring method cannot be effectively applied. Therefore, we propose a novel process monitoring framework, termed as mixture enhanced kernel canonical correlation analysis framework (M-NAKCCA). The innovations and advantages of M-NAKCCA are as follows: 1). The traditional CCA method is re-boosted as a new method, M-NAKCCA, to better nonlinear fault detection. Also, a matter-element model (MEm) is assimilated into M-NAKCCA to make the information more refined. 2). To overcome the curse of dimensionality that usually occurs in the high-dimensional dataset, M-NAKCCA uses the Nyström approximation technology to compress the kernel matrix. Moreover, the T2 control chart is reconstructed and the corresponding control upper limit is re-configured to improve the method sensitivity and to better the fault detection performance. 3). The proposed M-NAKCCA framework is firstly used to monitor a wastewater treatment plant (WWTP) and chemical plant with diverse process behaviors. The experimental results showed that the M-NAKCCA framework achieved the best performance for both of case studies.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/156192