dc.creatorAcuña, David E.
dc.creatorOrchard Concha, Marcos
dc.date.accessioned2019-05-29T13:10:21Z
dc.date.available2019-05-29T13:10:21Z
dc.date.created2019-05-29T13:10:21Z
dc.date.issued2017
dc.identifierMechanical Systems and Signal Processing 85 (2017) 827–848
dc.identifier10961216
dc.identifier08883270
dc.identifier10.1016/j.ymssp.2016.08.029
dc.identifierhttps://repositorio.uchile.cl/handle/2250/168798
dc.description.abstractThis paper presents a novel prognostic method that allows a proper characterization of the uncertainty associated with the evolution in time of nonlinear dynamical systems. The method assumes a state-space representation of the system, as well as the availability of particle-filtering-based estimates of the state posterior density at the moment in which the prognostic algorithm is executed. Our proposal significantly improves all particle-filtering- based prognosis frameworks currently available in two main aspects. First, it provides a correction for the expression that is used for the computation of the Time-of- Failure (ToF) probability mass function in the context of online monitoring schemes. Secondly, it presents a method for improved characterization of the tails of the ToF probability mass function via sequential propagation of sigma-points and the computation of Gaussian Mixture Models (GMMs). The proposed algorithm is tested and validated using experimental data related to the problem of Lithium-Ion battery State-of-Charge prognosis.
dc.languageen
dc.publisherElsevier
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
dc.sourceMechanical Systems and Signal Processing
dc.subjectBattery State-of-Charge
dc.subjectParticle filters
dc.subjectPrognostics and health management
dc.subjectUncertainty characterization
dc.titleParticle-filtering-based failure prognosis via sigma-points: Application to Lithium-Ion battery State-of-Charge monitoring
dc.typeArtículo de revista


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