Fault detection for uncertain LPV systems using probabilistic set-membership parity relation

This paper considers fault detection of uncertain linear parameter varying systems that have polynomial dependence on parametric uncertainties. A conventional set-membership (SM) approach is able to ensure zero false alarm rate (FAR) by using conservative threshold sets, but usually results in a hig...

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Autores: Wan, Yiming, Puig, Vicenç, Ocampo-Martínez, Carlos, Wang, Ye, Harinath, Eranda, Braatz, Richard D.
Tipo de documento: artigo
Estado:Versión aceptada para publicación
Data de publicação:2020
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/227637
Acesso em linha:http://hdl.handle.net/10261/227637
Access Level:Acceso aberto
Palavra-chave:Fault detection
Linear parameter varying systems
Probabilistic parametric uncertainties
Parity relationSet membership approach
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spelling Fault detection for uncertain LPV systems using probabilistic set-membership parity relationWan, YimingPuig, VicençOcampo-Martínez, CarlosWang, YeHarinath, ErandaBraatz, Richard D.Fault detectionLinear parameter varying systemsProbabilistic parametric uncertaintiesParity relationSet membership approachThis paper considers fault detection of uncertain linear parameter varying systems that have polynomial dependence on parametric uncertainties. A conventional set-membership (SM) approach is able to ensure zero false alarm rate (FAR) by using conservative threshold sets, but usually results in a high missed detection rate (MDR) due to equally treating all uncertainty realizations without distinguishing between high and low probability of occurrence. To address this limitation, a probabilistic SM parity relation approach is proposed to exploit probabilistic information on the parametric uncertainties, which results in a reduced MDR by admitting an acceptable FAR. The parity relation is first polynomially parameterized with respect to uncertain parameters. Then, Gaussian mixtures are adopted to efficiently compute uncertainty propagation from stochastic uncertainties to the residual distribution. To achieve an acceptable FAR, a non-convex confidence set of residuals – represented by a union of ellipsoids – is determined for the consistency test. The effectiveness of the proposed approach is illustrated using a continuous stirred tank reactor example including performance comparisons with a deterministic zonotope-based method.This work is supported by the National Natural Science Foundation of China, Grant No. 61803163 and No. 61603024. It is also partially supported by the Spanish project DEOCS (ref. MINECO DPI2016-76493).Elsevier BVNational Natural Science Foundation of ChinaMinisterio de Economía y Competitividad (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2021202120202021info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/227637reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2016-76493http://dx.doi.org/10.1016/j.jprocont.2019.12.010Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2276372026-05-22T06:33:51Z
dc.title.none.fl_str_mv Fault detection for uncertain LPV systems using probabilistic set-membership parity relation
title Fault detection for uncertain LPV systems using probabilistic set-membership parity relation
spellingShingle Fault detection for uncertain LPV systems using probabilistic set-membership parity relation
Wan, Yiming
Fault detection
Linear parameter varying systems
Probabilistic parametric uncertainties
Parity relationSet membership approach
title_short Fault detection for uncertain LPV systems using probabilistic set-membership parity relation
title_full Fault detection for uncertain LPV systems using probabilistic set-membership parity relation
title_fullStr Fault detection for uncertain LPV systems using probabilistic set-membership parity relation
title_full_unstemmed Fault detection for uncertain LPV systems using probabilistic set-membership parity relation
title_sort Fault detection for uncertain LPV systems using probabilistic set-membership parity relation
dc.creator.none.fl_str_mv Wan, Yiming
Puig, Vicenç
Ocampo-Martínez, Carlos
Wang, Ye
Harinath, Eranda
Braatz, Richard D.
author Wan, Yiming
author_facet Wan, Yiming
Puig, Vicenç
Ocampo-Martínez, Carlos
Wang, Ye
Harinath, Eranda
Braatz, Richard D.
author_role author
author2 Puig, Vicenç
Ocampo-Martínez, Carlos
Wang, Ye
Harinath, Eranda
Braatz, Richard D.
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv National Natural Science Foundation of China
Ministerio de Economía y Competitividad (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Fault detection
Linear parameter varying systems
Probabilistic parametric uncertainties
Parity relationSet membership approach
topic Fault detection
Linear parameter varying systems
Probabilistic parametric uncertainties
Parity relationSet membership approach
description This paper considers fault detection of uncertain linear parameter varying systems that have polynomial dependence on parametric uncertainties. A conventional set-membership (SM) approach is able to ensure zero false alarm rate (FAR) by using conservative threshold sets, but usually results in a high missed detection rate (MDR) due to equally treating all uncertainty realizations without distinguishing between high and low probability of occurrence. To address this limitation, a probabilistic SM parity relation approach is proposed to exploit probabilistic information on the parametric uncertainties, which results in a reduced MDR by admitting an acceptable FAR. The parity relation is first polynomially parameterized with respect to uncertain parameters. Then, Gaussian mixtures are adopted to efficiently compute uncertainty propagation from stochastic uncertainties to the residual distribution. To achieve an acceptable FAR, a non-convex confidence set of residuals – represented by a union of ellipsoids – is determined for the consistency test. The effectiveness of the proposed approach is illustrated using a continuous stirred tank reactor example including performance comparisons with a deterministic zonotope-based method.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/227637
url http://hdl.handle.net/10261/227637
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2016-76493
http://dx.doi.org/10.1016/j.jprocont.2019.12.010
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier BV
publisher.none.fl_str_mv Elsevier BV
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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