A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring.

Cheng, H; Liu, Y; Huang, D; Xu, C; Wu, J

A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring.

Cheng, H Liu, Y Huang, D Xu, C Wu, J

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Sensors (Basel, Switzerland), 2020, 20, (21), pp. 1-17
Issue Date:: 2020-10-28

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Field	Value	Language
dc.contributor.author	Cheng, H
dc.contributor.author	Liu, Y
dc.contributor.author	Huang, D
dc.contributor.author	Xu, C
dc.contributor.author	Wu, J
dc.date.accessioned	2021-06-18T01:25:32Z
dc.date.available	2020-10-26
dc.date.available	2021-06-18T01:25:32Z
dc.date.issued	2020-10-28
dc.identifier.citation	Sensors (Basel, Switzerland), 2020, 20, (21), pp. 1-17
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/149610
dc.description.abstract	Process monitoring plays an important role in ensuring the safety and stable operation of equipment in a large-scale process. This paper proposes a novel data-driven process monitoring framework, termed the ensemble adaptive sparse Bayesian transfer learning machine (EAdspB-TLM), for nonlinear fault diagnosis. The proposed framework has the following advantages: Firstly, the probabilistic relevance vector machine (PrRVM) under Bayesian framework is re-derived so that it can be used to forecast the plant operating conditions. Secondly, we extend the PrRVM method and assimilate transfer learning into the sparse Bayesian learning framework to provide it with the transferring ability. Thirdly, the source domain (SD) data are re-enabled to alleviate the issue of insufficient training data. Finally, the proposed EAdspB-TLM framework was effectively applied to monitor a real wastewater treatment process (WWTP) and a Tennessee Eastman chemical process (TECP). The results further demonstrate that the proposed method is feasible.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI AG
dc.relation.ispartof	Sensors (Basel, Switzerland)
dc.relation.isbasedon	10.3390/s20216139
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0502 Environmental Science and Management, 0602 Ecology, 0301 Analytical Chemistry, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.title	A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring.
dc.type	Journal Article
utslib.citation.volume	20
utslib.location.activity	Switzerland
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0301 Analytical Chemistry
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
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
utslib.copyright.status	open_access	*
dc.date.updated	2021-06-18T01:25:28Z
pubs.issue	21
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	21

Abstract:

Process monitoring plays an important role in ensuring the safety and stable operation of equipment in a large-scale process. This paper proposes a novel data-driven process monitoring framework, termed the ensemble adaptive sparse Bayesian transfer learning machine (EAdspB-TLM), for nonlinear fault diagnosis. The proposed framework has the following advantages: Firstly, the probabilistic relevance vector machine (PrRVM) under Bayesian framework is re-derived so that it can be used to forecast the plant operating conditions. Secondly, we extend the PrRVM method and assimilate transfer learning into the sparse Bayesian learning framework to provide it with the transferring ability. Thirdly, the source domain (SD) data are re-enabled to alleviate the issue of insufficient training data. Finally, the proposed EAdspB-TLM framework was effectively applied to monitor a real wastewater treatment process (WWTP) and a Tennessee Eastman chemical process (TECP). The results further demonstrate that the proposed method is feasible.

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http://hdl.handle.net/10453/149610