@Article{EchevarriaCamBecSilSan:2014:FaDiIn,
author = "Echevarria, L{\'{\i}}dice Camps and Campos Velho, Haroldo Fraga
de and Becceneri, Jose Carlos and Silva Neto, Antonio Jose da and
Santiago, Orestes Llanes",
affiliation = "CUJAE, Inst Super Politecn Jose Antonio Echeverria, Havana 19390,
Cuba. and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and IPRJ UERJ,
Inst Politecn, Nova Friburgo, RJ, Brazil. and CUJAE, Inst Super
Politecn Jose Antonio Echeverria, Havana 19390, Cuba.",
title = "The fault diagnosis inverse problem with Ant Colony Optimization
and Ant Colony Optimization with dispersion",
journal = "Applied Mathematics and Computation",
year = "2014",
volume = "227",
pages = "687--700",
month = "Jan.",
note = "{Appendix A. Supplementary data} and Supplementary data associated
with this article can be found, in the online version, at
http://dx.doi.org/10.1016/j.amc.2013.11.062.",
keywords = "Ant Colony Optimization, Fault diagnosis, Industrial systems,
Inverse problem, Robustness, Sensitivity, Structural
detectability, Structural separability.",
abstract = "This paper is focused on the formulation of fault diagnosis (FDI)
using an inverse problem methodology. The FDI inverse problem is
formulated as an optimization problem which is solved by
bio-inspired algorithms. In this case, the algorithms Ant Colony
Optimization (ACO), and its modified version ACO-d have been
applied. This approach combines results from FDI area for making
an alternative uniqueness analysis of the FDI inverse problem,
which is related with detectability and isolability of faults,
components of the diagnosis. The proposed approach is tested using
simulated data from the Inverted-Pendulum System which is
recognized as a benchmark for control and diagnosis. This work
also studies the influence of ACO and ACO-d parameters in order to
obtain a robust (to external disturbances) and sensitive (to
incipient faults) diagnosis. The results show the suitability of
the approach. They also indicate that parameters values allowing a
greater diversification of the search, yield a better diagnosis.
The ACO-d algorithm enables better diagnosis than ACO.",
doi = "10.1016/j.amc.2013.11.062",
url = "http://dx.doi.org/10.1016/j.amc.2013.11.062",
issn = "0096-3003",
label = "isi 2014-05 CampsEchevarriaCamBecSilLla:2014:FaDiIn",
language = "en",
targetfile = "1-s2.0-S0096300313012319-main.pdf",
url = "http://dx.doi.org/10.1016/j.amc.2013.11.062",
urlaccessdate = "27 abr. 2024"
}