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.",