Limitations of Genetic Programming Applied to Incipient Fault Detection: SFRA as Example

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Cerda:2015:CSCI,
  author =       "Jaime Cerda and Alberto Avalos and Mario Graff",
  booktitle =    "2015 International Conference on Computational Science
                 and Computational Intelligence (CSCI)",
  title =        "Limitations of Genetic Programming Applied to
                 Incipient Fault Detection: SFRA as Example",
  year =         "2015",
  pages =        "498--503",
  abstract =     "This document deals with the application of genetic
                 programming to the fault detection task, specifically
                 with the power transformer fault detection problem of
                 incipient faults. To this end we use genetic
                 programming to obtain an highly approximated model of
                 the a power transformer. The sweep frequency response
                 analysis test represents the response of the
                 transformer to a discrete variable frequency stimuli.
                 We have been able to obtain a highly precision model
                 which improves the precision of a commercial PG system.
                 This result would be good if we only needed to identify
                 the system. However, for the fault detection task, we
                 should be able to identify the components within the
                 transformer to assert where the fault has taken place.
                 This is because the SFRA test when an incipient fault
                 is present are similar but different as the fault
                 advance. The tree generated for the model after the
                 fault is evolved from the tree defining the power
                 transformer model before the fault. Both trees are
                 similar but the evolution seems to take place in a very
                 specific random place. There is no way we can relate
                 such changes with the physical model of the
                 transformer. This shows the limitations of genetic
                 programming to deal with this task and calls for
                 extensions to the genetic programming paradigm or the
                 merge of paradigms in order to deal with such task.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CSCI.2015.168",
  month =        dec,
  notes =        "Electr. Eng. Sch., UMSNH, Morelia, Mexico

                 Also known as \cite{7424143}",
}

Genetic Programming entries for Jaime Cerda Alberto Avalos Mario Graff Guerrero

Citations