Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Vista sencilla de ítem
dc.citation.journalTitleFRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERINGspa
dc.contributor.authorRuiz-Arenas S.
dc.contributor.authorRusák Z.
dc.contributor.authorHorváth I.
dc.contributor.authorMejí-Gutierrez R.
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería de Diseño
dc.contributor.researchgroupIngeniería de Diseño (GRID)spa
dc.date.accessioned2021-04-12T21:15:02Z
dc.date.available2021-04-12T21:15:02Z
dc.date.issued2019-01-01
dc.description.abstractMalfunction or breakdown of certain mission critical systems (MCSs) may cause losses of life, damage the environments, and/or lead to high costs. Therefore, recognition of emerging failures and preventive maintenance are essential for reliable operation of MCSs. There is a practical approach for identifying and forecasting failures based on the indicators obtained from real life processes. We aim to develop means for performing active failure diagnosis and forecasting based on monitoring statistical changes of generic signal features in the specific operation modes of the system. In this paper, we present a new approach for identifying emerging failures based on their manifestations in system signals. Our approach benefits from the dynamic management of the system operation modes and from simultaneous processing and characterization of multiple heterogeneous signal sources. It improves the reliability of failure diagnosis and forecasting by investigating system performance in various operation modes, includes reasoning about failures and forming of failures using a failure indicator matrix which is composed of statistical deviation of signal characteristics between normal and failed operations, and implements a failure indicator concept that can be used as a plug and play failure diagnosis and failure forecasting feature of cyber-physical systems. We demonstrate that our method can automate failure diagnosis in the MCSs and lend the MCSs to the development of decision support systems for preventive maintenance. © 2019, Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8614
dc.identifier.doi10.1631/FITEE.1700277
dc.identifier.issn20959184
dc.identifier.issn20959230
dc.identifier.otherWOS;000462004100002
dc.identifier.otherSCOPUS;2-s2.0-85063196562
dc.identifier.urihttp://hdl.handle.net/10784/28998
dc.language.isoengeng
dc.publisherZhejiang University
dc.relationDOI;10.1631/FITEE.1700277
dc.relationWOS;000462004100002
dc.relationSCOPUS;2-s2.0-85063196562
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85063196562&doi=10.1631%2fFITEE.1700277&partnerID=40&md5=ff9bcdaeb43067499c1743374b3f4d8a
dc.rightsZhejiang University
dc.sourceFRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
dc.subject.keywordArtificial intelligenceeng
dc.subject.keywordComputer aided diagnosiseng
dc.subject.keywordCyber Physical Systemeng
dc.subject.keywordDecision support systemseng
dc.subject.keywordEmbedded systemseng
dc.subject.keywordFailure analysiseng
dc.subject.keywordForecastingeng
dc.subject.keywordLarge scale systemseng
dc.subject.keywordFailure classificationeng
dc.subject.keywordFailure detectioneng
dc.subject.keywordMission critical systemseng
dc.subject.keywordSignal characteristiceng
dc.subject.keywordStatistical deviationseng
dc.subject.keywordSystem operation modeseng
dc.subject.keywordSystematic explorationeng
dc.subject.keywordTP277eng
dc.subject.keywordPreventive maintenanceeng
dc.titleSystematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systemseng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

Archivos

Bloque original
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
FITEE.1700277.pdf
Tamaño:
1.54 MB
Formato:
Adobe Portable Document Format
Descripción:
Descargar

Colecciones

Articulos