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Article: A nonlinear process monitoring approach with locally weighted learning of available data

TitleA nonlinear process monitoring approach with locally weighted learning of available data
Authors
Issue Date2017
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=41
Citation
IEEE Transactions on Industrial Electronics, 2017, v. 64, p. 1507-1516 How to Cite?
AbstractThis paper proposes a data-driven approach for nonlinear process monitoring under the framework of locally weighted learning. Based on available process measurements, the locally weighted projection regression is used in the offline learning scheme to provide a series of locally weighted linear models, in which the algorithms of traditional projection to latent structures (PLS) and total PLS could be applied to establish improved test statistics suitable for complicated process monitoring. By using the weights of local models obtained from measurement learning, the developed test statistics are further online utilized to monitor potential abnormalities related or unrelated to process quality. The effectiveness of the proposed locally weighted total PLS monitoring approach is finally demonstrated by the comparisons with other relevant methods via simulations based on the wastewater treatment process benchmark under different abnormal conditions.
Persistent Identifierhttp://hdl.handle.net/10722/266443
ISSN
2017 Impact Factor: 7.05
2015 SCImago Journal Rankings: 3.285
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYin, S-
dc.contributor.authorXIE, X-
dc.contributor.authorSun, W-
dc.date.accessioned2019-01-18T08:19:44Z-
dc.date.available2019-01-18T08:19:44Z-
dc.date.issued2017-
dc.identifier.citationIEEE Transactions on Industrial Electronics, 2017, v. 64, p. 1507-1516-
dc.identifier.issn0278-0046-
dc.identifier.urihttp://hdl.handle.net/10722/266443-
dc.description.abstractThis paper proposes a data-driven approach for nonlinear process monitoring under the framework of locally weighted learning. Based on available process measurements, the locally weighted projection regression is used in the offline learning scheme to provide a series of locally weighted linear models, in which the algorithms of traditional projection to latent structures (PLS) and total PLS could be applied to establish improved test statistics suitable for complicated process monitoring. By using the weights of local models obtained from measurement learning, the developed test statistics are further online utilized to monitor potential abnormalities related or unrelated to process quality. The effectiveness of the proposed locally weighted total PLS monitoring approach is finally demonstrated by the comparisons with other relevant methods via simulations based on the wastewater treatment process benchmark under different abnormal conditions.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=41-
dc.relation.ispartofIEEE Transactions on Industrial Electronics-
dc.rightsIEEE Transactions on Industrial Electronics. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleA nonlinear process monitoring approach with locally weighted learning of available data-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TIE.2016.2612161-
dc.identifier.hkuros296626-
dc.identifier.volume64-
dc.identifier.spage1507-
dc.identifier.epage1516-
dc.identifier.isiWOS:000395826100064-
dc.publisher.placeUnited States-

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