Fault prognostics of rolling bearings using a hybrid approach

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dc.creatorMurilo Cesar Osorio Camargos Filho
dc.creatorIury Valente de Bessa
dc.creatorMarcos Flavio S. V. D'Angelo
dc.creatorReinaldo Martinez Palhares
dc.date.accessioned2025-05-23T13:05:41Z
dc.date.accessioned2025-09-09T01:00:49Z
dc.date.available2025-05-23T13:05:41Z
dc.date.issued2020
dc.identifier.doihttps://doi.org/10.1016/j.ifacol.2020.12.2435
dc.identifier.urihttps://hdl.handle.net/1843/82470
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartof21st IFAC World Congress
dc.rightsAcesso Restrito
dc.subjectRolamento de esferas
dc.subject.otherPrognostics, health management, remaining useful life, particle filter, hybrid prognostics, accelerated ball bearings
dc.titleFault prognostics of rolling bearings using a hybrid approach
dc.typeArtigo de evento
local.description.resumoThis paper presents a two-phase hybrid prognostics approach; in the first phase, the model’s parameters are estimated using available training data in the least squares sense using the Levenberg-Marquardt algorithm. The second phase consists of using a particle filter to update the knowledge acquired so far and to predict future states of the system using in the Bayesian sense. The approach is used for an accelerated ball bearing data set, the PRONOSTIA platform, where a general fractional polynomial model is proposed as degradation model. The results of the Remaining Useful Life estimation are compared with another work in the literature, indicating its suitability and competitiveness for prognostics in this data set.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S2405896320331177

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