A fast feature selection algorithm applied to automatic faults diagnosis of rotating machinery

Authors

  • Francisco de Assis Boldt Universidade Federal do Espírito Santo
  • Thomas Walter Rauber Universidade Federal do Espírito Santo
  • Flávio Miguel Varejão Universidade Federal do Espírito Santo

DOI:

https://doi.org/10.4013/jacr.2013.32.02

Abstract

This work presents a fast algorithm to reduce the number of features of a classification system increasing the performance without loss of quality. The experiments show that the proposed algorithm can reduce the number of features quickly as well as increase the quality of the predictions simultaneously. Three features extractions were used to generate the initial pool of features of the system. Comparative results of the proposed algorithm with the classical sequential forward selection algorithm are shown.

Keywords: feature selection, feature extraction, fault diagnosis, rotating machinery, supervised learning.

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Published

2014-09-04

Issue

Vol. 3 No. 2 (2013): Jul-Dec

Section

Articles

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