TITLE

Clasificador neuronal de fallos en rodamientos utilizando entradas basadas en transformadas wavelet packet y de Fourier

AUTHOR(S)
Gómez, Víctor; Moreno, Ricardo
PUB. DATE
June 2013
SOURCE
Revista Facultad de Ingenieria Universidad de Antioquia;jun2013, Issue 67, p126
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
In this paper one method for bearings diagnosis is proposed and evaluated. This method use signal pattern recognition from mechanical vibrations. Wavelet and Fourier transforms are used for pre-processing the signal and an Artificial Neural Network (ANN) is used as a classifier. Analysis of variance (ANOVA) is used for evaluating the ANN inputs. ANOVA is performed to compare the effect of the factors: speed, load, outer race fault and rolling element fault on each of the parameters proposed as inputs of the ANN, looking for the best parameters for classifying the faults. About 2000 ANN structures were trained in order to find the most appropriate classifier. The results show that the average of success in classifying was 88,5 % for the scaled conjugate gradient algorithm (trainscg), while the Levenberg Marquardt algorithm (trainlm) presented 91,8 %. Besides, it was possible to achieve 100 % of success in classifying in 7 cases.
ACCESSION #
97255055

 

Related Articles

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics