Development of multiple-step soft-sensors using a Gaussian process model with application for fault prognosis

Liu, Y; Xiao, H; Pan, Y; Huang, D; Wang, Q

Development of multiple-step soft-sensors using a Gaussian process model with application for fault prognosis

Liu, Y Xiao, H Pan, Y Huang, D Wang, Q

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Publisher:: ELSEVIER SCIENCE BV
Publication Type:: Journal Article
Citation:: Chemometrics and Intelligent Laboratory Systems, 2016, 157, pp. 85-95
Issue Date:: 2016-10-15

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Field	Value	Language
dc.contributor.author	Liu, Y
dc.contributor.author	Xiao, H
dc.contributor.author	Pan, Y
dc.contributor.author	Huang, D
dc.contributor.author	Wang, Q https://orcid.org/0000-0002-5744-2331
dc.date.accessioned	2022-08-02T22:45:38Z
dc.date.available	2022-08-02T22:45:38Z
dc.date.issued	2016-10-15
dc.identifier.citation	Chemometrics and Intelligent Laboratory Systems, 2016, 157, pp. 85-95
dc.identifier.issn	0169-7439
dc.identifier.issn	1873-3239
dc.identifier.uri	http://hdl.handle.net/10453/159511
dc.description.abstract	Predicting the degradation of working conditions and trending of fault propagation before they reach the alarm or failure control limit is significantly important to optimize the operational capacity of a chemical process. However, traditional one-step-ahead (OS) soft-sensors render such benefits inadequate. Direct, Recursive and Direct-recursive strategies are proposed to generalize the Gaussian Process Regression (GPR) model for multi-step-ahead (MS) prediction, thereby supporting the fault diagnosis and prognosis of the product qualities control for chemical processes. The proposed methodology was firstly demonstrated by applying the designed algorithm to a wastewater plant (WWTP) simulated with the well-established model, i.e., Benchmark Simulation Model 1 (BSM1), then extended to a full-scale WWTP with data collected from the field influenced by filamentous sludge bulking. Results showed that the proposed strategies significantly improved the prediction performance.
dc.language	English
dc.publisher	ELSEVIER SCIENCE BV
dc.relation.ispartof	Chemometrics and Intelligent Laboratory Systems
dc.relation.isbasedon	10.1016/j.chemolab.2016.07.002
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0301 Analytical Chemistry
dc.subject.classification	Analytical Chemistry
dc.title	Development of multiple-step soft-sensors using a Gaussian process model with application for fault prognosis
dc.type	Journal Article
utslib.citation.volume	157
utslib.for	0102 Applied Mathematics
utslib.for	0301 Analytical Chemistry
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
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-02T22:45:36Z
pubs.publication-status	Published
pubs.volume	157

Abstract:

Predicting the degradation of working conditions and trending of fault propagation before they reach the alarm or failure control limit is significantly important to optimize the operational capacity of a chemical process. However, traditional one-step-ahead (OS) soft-sensors render such benefits inadequate. Direct, Recursive and Direct-recursive strategies are proposed to generalize the Gaussian Process Regression (GPR) model for multi-step-ahead (MS) prediction, thereby supporting the fault diagnosis and prognosis of the product qualities control for chemical processes. The proposed methodology was firstly demonstrated by applying the designed algorithm to a wastewater plant (WWTP) simulated with the well-established model, i.e., Benchmark Simulation Model 1 (BSM1), then extended to a full-scale WWTP with data collected from the field influenced by filamentous sludge bulking. Results showed that the proposed strategies significantly improved the prediction performance.

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http://hdl.handle.net/10453/159511