dc.creator | Acuña, David E. | |
dc.creator | Orchard Concha, Marcos | |
dc.date.accessioned | 2019-05-29T13:10:21Z | |
dc.date.available | 2019-05-29T13:10:21Z | |
dc.date.created | 2019-05-29T13:10:21Z | |
dc.date.issued | 2017 | |
dc.identifier | Mechanical Systems and Signal Processing 85 (2017) 827–848 | |
dc.identifier | 10961216 | |
dc.identifier | 08883270 | |
dc.identifier | 10.1016/j.ymssp.2016.08.029 | |
dc.identifier | https://repositorio.uchile.cl/handle/2250/168798 | |
dc.description.abstract | This 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.language | en | |
dc.publisher | Elsevier | |
dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Chile | |
dc.source | Mechanical Systems and Signal Processing | |
dc.subject | Battery State-of-Charge | |
dc.subject | Particle filters | |
dc.subject | Prognostics and health management | |
dc.subject | Uncertainty characterization | |
dc.title | Particle-filtering-based failure prognosis via sigma-points: Application to Lithium-Ion battery State-of-Charge monitoring | |
dc.type | Artículo de revista | |