A novel fault-prognostic approach based on interacting multiple model filters and fuzzy systems

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dc.creatorLuciana Balieiro Cosme
dc.creatorWalmir Matos Caminhas
dc.creatorMarcos Flávio Silveira Vasconcelos D'Angelo
dc.creatorReinaldo Martinez Palhares
dc.date.accessioned2025-05-22T12:52:58Z
dc.date.accessioned2025-09-08T22:56:50Z
dc.date.available2025-05-22T12:52:58Z
dc.date.issued2019
dc.identifier.doihttps://doi.org/10.1109/TIE.2018.2826449
dc.identifier.issn0278-0046
dc.identifier.urihttps://hdl.handle.net/1843/82442
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofIEEE Transactions on industrial electronics
dc.rightsAcesso Restrito
dc.subjectSistemas difusos
dc.subjectEngenharia de Produção
dc.subject.otherFuzzy systems
dc.subject.otherFault prognostic
dc.subject.otherInteracting multiple model (IMM) filter
dc.subject.otherControle
dc.subject.otherInteligência Computacional
dc.subject.otherInteligência Artificial
dc.titleA novel fault-prognostic approach based on interacting multiple model filters and fuzzy systems
dc.typeArtigo de periódico
local.citation.epage528
local.citation.issue1
local.citation.spage519
local.citation.volume66
local.description.resumoAn interacting multiple model (IMM) filter is a recognized method for adaptive estimation of states that is often necessary to characterize the behavior of dynamic systems with a multiple-mode operation. The traditional IMM filter adopts the measurement set to update the information about the active models. However, when this approach is used for fault-prognostic applications, it can lead to misleading state estimation. To overcome this problem, this paper proposes a novel fault-prognostic technique based on a modified IMM filter with fuzzy systems. The main methodological contribution of this paper is to build an approach based on an IMM filter able to make long-term fault predictions without measurements. This is done by incorporating a fuzzy system into the proposed IMM filter with the objective of modeling the system's dynamics and updating the probabilities of the observed modes. Furthermore, the proposed method and the standard IMM filter are compared in a numerical example and a real experimental platform PRONOSTIA for validation. The results analysis indicates a better prediction performance than the conventional IMM filter considering the failure time predictions and a measurement model not properly adjusted.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://ieeexplore.ieee.org/document/8336959

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