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Hidden Markov Model for the detection of a degraded operating mode of optronic equipment

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.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorGÉGOUT-PETIT, Anne
hal.structure.identifierThalès Optronique
dc.contributor.authorPRENAT, Michel
hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierEcole Nationale Supérieure de Cognitique [ENSC]
dc.contributor.authorSARACCO, Jérôme
dc.date.accessioned2024-04-04T02:23:41Z
dc.date.available2024-04-04T02:23:41Z
dc.date.created2012-12-11
dc.date.issued2014
dc.identifier.issn1962-5197
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/189754
dc.description.abstractEnAs part of optimizing the reliability, Thales Optronics now includes systems that examine the state of its equipment. The aim of this paper is to use hidden Markov Model to detect as soon as possible a change of state of optronic equipment in order to propose maintenance before failure. For this, we carefully observe the dynamic of a variable called "cool down time" and noted Tmf, which reflects the state of the cooling system. Indeed, the Tmf is an indirect observation of the hidden state of the system. This one is modelled by a Markov chain and the Tmf is a noisy function of it. Thanks to filtering equations, we obtain results on the probability that an appliance is in degraded state at time $t$, knowing the history of the Tmf until this moment. We have evaluated the numerical behavior of our approach on simulated data. Then we have applied this methodology on our real data and we have checked that the results are consistent with the reality. This method can be implemented in a HUMS (Health and Usage Monitoring System). This simple example of HUMS would allow the Thales Optronics Company to improve its maintenance system. This company will be able to recall appliances which are estimated to be in degraded state and do not control to soon those estimated in stable state.
dc.language.isoen
dc.publisherSociété Française de Statistique et Société Mathématique de France
dc.subjectmaintenance
dc.subjectHUMS
dc.subjectfiabilité
dc.subjectfiltrage
dc.subjectchaîne de Markov cachée
dc.subjectDétection de rupture
dc.title.enHidden Markov Model for the detection of a degraded operating mode of optronic equipment
dc.typeArticle de revue
dc.subject.halStatistiques [stat]/Applications [stat.AP]
dc.identifier.arxiv1212.2358
bordeaux.journalJournal de la Société Française de Statistique
bordeaux.pageA paraître
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-00763963
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00763963v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20de%20la%20Socie%CC%81te%CC%81%20Franc%CC%A7aise%20de%20Statistique&rft.date=2014&rft.spage=A%20para%C3%AEtre&rft.epage=A%20para%C3%AEtre&rft.eissn=1962-5197&rft.issn=1962-5197&rft.au=BAYSSE,%20Camille&BIHANNIC,%20Didier&G%C3%89GOUT-PETIT,%20Anne&PRENAT,%20Michel&SARACCO,%20J%C3%A9r%C3%B4me&rft.genre=article


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