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dc.contributor.authorCheng, H
dc.contributor.authorLiu, Y
dc.contributor.authorHuang, D
dc.contributor.authorCai, B
dc.contributor.authorWang, Q
dc.date.accessioned2021-04-15T03:45:52Z
dc.date.available2021-04-15T03:45:52Z
dc.date.issued2021
dc.identifier.issn0957-5820
dc.identifier.doi10.1016/j.psep.2021.03.025
dc.identifier.urihttp://hdl.handle.net/10072/403776
dc.description.abstractProcess 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 T 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. 2
dc.description.peerreviewedYes
dc.languageen
dc.publisherElsevier BV
dc.relation.ispartofpagefrom619
dc.relation.ispartofpageto630
dc.relation.ispartofjournalProcess Safety and Environmental Protection
dc.relation.ispartofvolume149
dc.subject.fieldofresearchChemical Engineering
dc.subject.fieldofresearchResources Engineering and Extractive Metallurgy
dc.subject.fieldofresearchApplied Mathematics
dc.subject.fieldofresearchMaritime Engineering
dc.subject.fieldofresearchcode0904
dc.subject.fieldofresearchcode0914
dc.subject.fieldofresearchcode0102
dc.subject.fieldofresearchcode0911
dc.titleRebooting kernel CCA method for nonlinear quality-relevant fault detection in process industries
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationCheng, H; Liu, Y; Huang, D; Cai, B; Wang, Q, Rebooting kernel CCA method for nonlinear quality-relevant fault detection in process industries, Process Safety and Environmental Protection, 2021, 149, pp. 619-630
dc.date.updated2021-04-15T03:45:01Z
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Qilin


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