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Analysis of Quality Measurements to Categorize Anomalies in Sensor Systems

hal.structure.identifierLab-STICC_IMTA_CID_DECIDE
hal.structure.identifierDépartement lmage et Traitement Information [IMT Atlantique - ITI]
hal.structure.identifierChaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS)
dc.contributor.authorMERINO LASO, Pedro
hal.structure.identifierInstitut de Recherche de l'Ecole Navale [IRENAV]
hal.structure.identifierChaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS)
dc.contributor.authorBROSSET, David
hal.structure.identifierLab-STICC_IMTA_CID_DECIDE
hal.structure.identifierDépartement lmage et Traitement Information [IMT Atlantique - ITI]
hal.structure.identifierChaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS)
dc.contributor.authorPUENTES, John
dc.date.accessioned2021-05-14T09:50:19Z
dc.date.available2021-05-14T09:50:19Z
dc.date.issued2017
dc.date.conference2017-07-18
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/77275
dc.description.abstractEnSensor networks are becoming ubiquitous, enabling to improve decision-making and reducing human interaction by means of automatic or semi-automatic responses. However, due to deterioration or induced effects, sensors measures can be affected and produce anomalies that could alter decision-making. Most of the existing methods to identify sensors irregularities focus basically on detecting and discarding anomalous values, without looking for complementary information to understand generated anomalies. This paper presents an approach to obtain such complementary information by categorizing sensor anomalies, based on multidimensional quality assessment. It consists of two processing stages: an evaluation of data and information streams to estimate data quality imperfections and information quality dimensions; followed by the determination of agreement limits, compliant with normal states, to identify and categorize anomalies. The case study of discrete and analog sensors system installed in a simulator training platform of fuel tanks is presented, to illustrate an application of the proposed approach, considering 13 experimentally evaluated anomalies.
dc.language.isoen
dc.publisherIEEE
dc.source.titleProceedings Computing 2017 : Science and Information Conference
dc.subject.enSensor
dc.subject.enAnomaly categorization
dc.subject.enData quality
dc.subject.enInformation quality
dc.subject.enCyber-physical system
dc.title.enAnalysis of Quality Measurements to Categorize Anomalies in Sensor Systems
dc.typeCommunication dans un congrès avec actes
dc.identifier.doi10.1109/SAI.2017.8252263
dc.subject.halInformatique [cs]/Automatique
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halInformatique [cs]/Performance et fiabilité [cs.PF]
dc.subject.halSciences de l'ingénieur [physics]/Traitement du signal et de l'image
bordeaux.page1330 - 1338
bordeaux.hal.laboratoriesInstitut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.institutionINRAE
bordeaux.institutionArts et Métiers
bordeaux.countryGB
bordeaux.title.proceedingComputing 2017 : Science and Information Conference
bordeaux.conference.cityLondres
bordeaux.peerReviewedoui
hal.identifierhal-01597458
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01597458v1
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