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- Publisher Website: 10.1109/TIE.2016.2612161
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Article: A nonlinear process monitoring approach with locally weighted learning of available data
Title | A nonlinear process monitoring approach with locally weighted learning of available data |
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Authors | |
Keywords | Data-driven locally weighted learning nonlinear systems process monitoring |
Issue Date | 2017 |
Publisher | Institute 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? |
Abstract | This 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 Identifier | http://hdl.handle.net/10722/266443 |
ISSN | 2023 Impact Factor: 7.5 2023 SCImago Journal Rankings: 3.395 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yin, S | - |
dc.contributor.author | XIE, X | - |
dc.contributor.author | Sun, W | - |
dc.date.accessioned | 2019-01-18T08:19:44Z | - |
dc.date.available | 2019-01-18T08:19:44Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IEEE Transactions on Industrial Electronics, 2017, v. 64, p. 1507-1516 | - |
dc.identifier.issn | 0278-0046 | - |
dc.identifier.uri | http://hdl.handle.net/10722/266443 | - |
dc.description.abstract | This 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.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=41 | - |
dc.relation.ispartof | IEEE Transactions on Industrial Electronics | - |
dc.rights | IEEE 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.subject | Data-driven | - |
dc.subject | locally weighted learning | - |
dc.subject | nonlinear systems | - |
dc.subject | process monitoring | - |
dc.title | A nonlinear process monitoring approach with locally weighted learning of available data | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TIE.2016.2612161 | - |
dc.identifier.scopus | eid_2-s2.0-85014854688 | - |
dc.identifier.hkuros | 296626 | - |
dc.identifier.volume | 64 | - |
dc.identifier.spage | 1507 | - |
dc.identifier.epage | 1516 | - |
dc.identifier.isi | WOS:000395826100064 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0278-0046 | - |