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Detection of a degraded operating mode of optronic equipment using Hidden Markov Model.

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorGÉGOUT-PETIT, Anne
hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierThalès Optronique
dc.contributor.authorBAYSSE, Camille
hal.structure.identifierThalès Optronique
dc.contributor.authorBIHANNIC, Didier
hal.structure.identifierThalès Optronique
dc.contributor.authorPRENAT, Michel
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorSARACCO, Jérôme
dc.date.accessioned2024-04-04T02:23:45Z
dc.date.available2024-04-04T02:23:45Z
dc.date.created2012-06
dc.date.issued2012-06
dc.date.conference2012-06-25
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/189762
dc.description.abstractEnAs part of optimizing the reliability, Thales Optronics now includes systems that examine the state of its equipment. This function is performed by HUMS (Health & Usage Monitoring System). We hope to implement a program based on these observations that can determine the lifetime of this optronic equipment. Our study focuses on a simple example of HUMS. As part of our research, we are interested in a variable called "time-to cold" noted TMF, which reflects the state of system. Using this information about this variable, we seek to detect as soon as possible a degraded state and propose maintenance before failure. This would allow the Thales Optronics Company to improve its maintenance system and achieve many economies. For this we use a hidden Markov model. The state of our system at time t is then modeled by a Markov chain X(t). However we do not observe directly this chain but indirectly through the TMF, a noisy function of this chain. Thanks to filtering equations, we obtained results on the probability that an equipment breaking down at time t, knowing the history of the TMF until this moment. We have subsequently presented these results based on simulated data. Then finally we applied these results on the analysis of our real data and we have checked that the results are consistent with the reality. So using this method could allow the company to recall equipments which are estimated in deteriorated state and do not control those estimated in stable state. Thales Optronics could improve its maintenance system and reduce its cost function.
dc.language.isoen
dc.source.titlePSAM 11 / ESREL 2012
dc.subjectHUMS
dc.subjectHidden Markov Model
dc.subjectDetection
dc.subjectestimation
dc.subjectsimulation
dc.title.enDetection of a degraded operating mode of optronic equipment using Hidden Markov Model.
dc.typeCommunication dans un congrès
dc.subject.halMathématiques [math]/Statistiques [math.ST]
dc.subject.halStatistiques [stat]/Théorie [stat.TH]
bordeaux.page6p
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titlePSAM 11 / ESREL 2012
bordeaux.countryFI
bordeaux.title.proceedingPSAM 11 / ESREL 2012
bordeaux.peerReviewedoui
hal.identifierhal-00762227
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2012-06-29
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00762227v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=PSAM%2011%20/%20ESREL%202012&rft.date=2012-06&rft.spage=6p&rft.epage=6p&rft.au=G%C3%89GOUT-PETIT,%20Anne&BAYSSE,%20Camille&BIHANNIC,%20Didier&PRENAT,%20Michel&SARACCO,%20J%C3%A9r%C3%B4me&rft.genre=unknown


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