The proposed methodology is capable of classifying the
failures with a high success rate, to the point that only one
sample avoided obtaining 100% accuracy. The effectiveness
of the methodology can be seen with also achieved a mean
accuracy of 99.55% for the classification of 10 different
bearing system failures.
V. CONCLUSIONS
This article presents a methodology for the diagnosis of
bearing failures based on the Multiscalar Permutation Entropy
(MPE) technique. The MPE proves to be a highly effective
characterization methodology to find information that allows
to separate classes. Specifically, in the mechanical vibration
signals that have a high non-stationary, the MPE manages to
find characteristics that would not be detected by other
methodologies. The MPE measures the non-linear dynamics
existing in non-stationary time series and when combined with
Relieff as a feature selection technique, a robust tool for
classification applications is obtained. For the classification a
method of K-Neighbors Nearest (KNN) was used, which
manages to adapt to the nature of the characteristics. The
results confirm a classification accuracy of more than 99:9%
with a computation time of 16:37 seconds, which exceeds the
results established in the literature.
ACKNOWLEDGMENT
The authors thank the Master in Electrical and Mechanical
Engineering of the Technological University of Pereira for
their support throughout the investigation. In addition, we
would like to thank COLCIENCIAS for supporting the project
entitled: “Desarrollo de un sistema de monitoreo para el
análisis energético y de condición de emisiones en motores de
combustión interna diésel con base en técnicas no
destructivas” with code 1110-776-57801, through which the
research described in this article was developed.
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