Show simple item record

dc.contributor.authorCheng, H
dc.contributor.authorLiu, Y
dc.contributor.authorHuang, D
dc.contributor.authorPan, Y
dc.contributor.authorWang, Q
dc.date.accessioned2021-01-06T04:41:14Z
dc.date.available2021-01-06T04:41:14Z
dc.date.issued2020
dc.identifier.issn0888-5885
dc.identifier.doi10.1021/acs.iecr.0c04885
dc.identifier.urihttp://hdl.handle.net/10072/400714
dc.description.abstractMultivariate statistical methods have gained significant popularity in past decades. However, process dynamics and insufficient training data usually result in degradation or even failure of a trained model. To deal with these problems, this paper proposes a novel process monitoring method, called cross-spatiotemporal adaptive boosting transfer learning (CS-AdBoostTrLM). Different from the standard methods, CS-AdBoostTrLM has the following advantages: first, source domain (SD) data, which are discarded by the factory, can be re-enabled to alleviate the issue of insufficient training data. Second, cross-spatiotemporal canonical correlation analysis is proposed to achieve the domain adaptation between the SD data and target domain data, so as to overcome the negative transfer. Third, the particle swarm optimization algorithm is used to optimize the local detection model, in such a way that the integrated detection model can converge to the optimality globally. Finally, the data from the wastewater treatment plant and chemical plant are analyzed to demonstrate the effectiveness of the proposed method.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherAmerican Chemical Society (ACS)
dc.relation.ispartofpagefrom21602
dc.relation.ispartofpageto21614
dc.relation.ispartofissue49
dc.relation.ispartofjournalIndustrial and Engineering Chemistry Research
dc.relation.ispartofvolume59
dc.subject.fieldofresearchChemical sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode34
dc.subject.fieldofresearchcode40
dc.titleAdaptive Transfer Learning of Cross-Spatiotemporal Canonical Correlation Analysis for Plant-Wide Process Monitoring
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationCheng, H; Liu, Y; Huang, D; Pan, Y; Wang, Q, Adaptive Transfer Learning of Cross-Spatiotemporal Canonical Correlation Analysis for Plant-Wide Process Monitoring, Industrial and Engineering Chemistry Research, 2020, 59 (49), pp. 21602-21614
dc.date.updated2021-01-06T04:36:28Z
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Qilin


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record