Multiple adaptive mechanisms for data-driven soft sensors

Bakirov, R; Gabrys, B; Fay, D

Multiple adaptive mechanisms for data-driven soft sensors

Bakirov, R Gabrys, B

Fay, D

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Computers and Chemical Engineering, 2017, 96, pp. 42-54
Issue Date:: 2017-01-04

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Field	Value	Language
dc.contributor.author	Bakirov, R
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.contributor.author	Fay, D
dc.date.accessioned	2022-08-02T05:25:23Z
dc.date.available	2022-08-02T05:25:23Z
dc.date.issued	2017-01-04
dc.identifier.citation	Computers and Chemical Engineering, 2017, 96, pp. 42-54
dc.identifier.issn	0098-1354
dc.identifier.issn	1873-4375
dc.identifier.uri	http://hdl.handle.net/10453/159486
dc.description.abstract	Recent data-driven soft sensors often use multiple adaptive mechanisms to cope with non-stationary environments. These mechanisms are usually deployed in a prescribed order which does not change. In this work we use real world data from the process industry to compare deploying adaptive mechanisms in a fixed manner to deploying them in a flexible way, which results in varying adaptation sequences. We demonstrate that flexible deployment of available adaptive methods coupled with techniques such as cross-validatory selection and retrospective model correction can benefit the predictive accuracy over time. As a vehicle for this study, we use a soft-sensor for batch processes based on an adaptive ensemble method which employs several adaptive mechanisms to react to the changes in data.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Computers and Chemical Engineering
dc.relation.isbasedon	10.1016/j.compchemeng.2016.08.017
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0904 Chemical Engineering, 0913 Mechanical Engineering
dc.subject.classification	Chemical Engineering
dc.title	Multiple adaptive mechanisms for data-driven soft sensors
dc.type	Journal Article
utslib.citation.volume	96
utslib.for	0904 Chemical Engineering
utslib.for	0913 Mechanical 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/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-02T05:25:21Z
pubs.publication-status	Published
pubs.volume	96

Abstract:

Recent data-driven soft sensors often use multiple adaptive mechanisms to cope with non-stationary environments. These mechanisms are usually deployed in a prescribed order which does not change. In this work we use real world data from the process industry to compare deploying adaptive mechanisms in a fixed manner to deploying them in a flexible way, which results in varying adaptation sequences. We demonstrate that flexible deployment of available adaptive methods coupled with techniques such as cross-validatory selection and retrospective model correction can benefit the predictive accuracy over time. As a vehicle for this study, we use a soft-sensor for batch processes based on an adaptive ensemble method which employs several adaptive mechanisms to react to the changes in data.

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http://hdl.handle.net/10453/159486