Implementation of system operation modes for health management and failure prognosis in cyber-physical systems

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dc.citation.journalTitleSensorsspa
dc.contributor.authorRuiz-Arenas, S.
dc.contributor.authorRusák, Z.
dc.contributor.authorMejía-Gutierrez, R.
dc.contributor.authorHorváth, I.
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería de Diseño
dc.contributor.researchgroupIngeniería de Diseño (GRID)spa
dc.date.accessioned2021-04-12T21:15:03Z
dc.date.available2021-04-12T21:15:03Z
dc.date.issued2020-01-01
dc.description.abstractCyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Despite the advantages that control mechanisms convey, they bring new challenges in terms of failure prevention. The compensatory action the control exerts cause a fault masking effect, hampering fault diagnosis. Likewise, the multiple information inputs CPSs have to process can affect the timely system response to faults. This article proposes a failure prognosis method, which combines time series-based forecasting methods with statistically based classification techniques in order to investigate system degradation and failure forming on system levels. This method utilizes a new approach based on the concept of the system operation mode (SOM) that offers a novel perspective for health management that allows monitoring the system behavior, through the frequency and duration of SOMs. Validation of this method was conducted by systematically injecting faults in a cyber-physical greenhouse testbed. The obtained results demonstrate that the degradation and fault forming process can be monitored by analyzing the changes of the frequency and duration of SOMs. These indicators made possible to estimate the time to failure caused by various failures in the conducted experiments. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=11939
dc.identifier.doi10.3390/s20082429
dc.identifier.issn14248220
dc.identifier.otherWOS;000533346400271
dc.identifier.otherPUBMED;32344705
dc.identifier.otherSCOPUS;2-s2.0-85083968709
dc.identifier.urihttp://hdl.handle.net/10784/29008
dc.language.isoengeng
dc.publisherMDPI AG
dc.relationDOI;10.3390/s20082429
dc.relationWOS;000533346400271
dc.relationPUBMED;32344705
dc.relationSCOPUS;2-s2.0-85083968709
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85083968709&doi=10.3390%2fs20082429&partnerID=40&md5=d147540aaa1fe4216658332080e7b54e
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/1424-8220
dc.sourceSensors
dc.subject.keywordEmbedded systemseng
dc.subject.keywordFailure (mechanical)eng
dc.subject.keywordClassification techniqueeng
dc.subject.keywordCyber physical systems (CPSs)eng
dc.subject.keywordFailure preventioneng
dc.subject.keywordForecasting methodseng
dc.subject.keywordHealth managementeng
dc.subject.keywordSystem degradationeng
dc.subject.keywordSystem operation modeseng
dc.subject.keywordVarying working conditionseng
dc.subject.keywordCyber Physical Systemeng
dc.subject.keywordarticleeng
dc.subject.keywordcontrolled studyeng
dc.subject.keywordforecastingeng
dc.subject.keywordgreenhouseeng
dc.subject.keywordhealth care managementeng
dc.subject.keywordhumaneng
dc.subject.keywordmaskingeng
dc.subject.keywordprognosiseng
dc.subject.keywordreliabilityeng
dc.subject.keywordtime series analysiseng
dc.subject.keywordvalidation processeng
dc.titleImplementation of system operation modes for health management and failure prognosis in cyber-physical systemseng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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