SciELO - Scientific Electronic Library Online

 
 número67Analysis of the dynamic behavior of a vehicle with fully independent double-wishbone suspension and anti-roll barsSynthesis of two new Nickel and Copper- Nickel vanadates used for propane oxidative dehydrogenation índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Artigo

Indicadores

Links relacionados

  • Em processo de indexaçãoCitado por Google
  • Não possue artigos similaresSimilares em SciELO
  • Em processo de indexaçãoSimilares em Google

Compartilhar


Revista Facultad de Ingeniería Universidad de Antioquia

versão impressa ISSN 0120-6230

Resumo

GOMEZ, Víctor  e  MORENO, Ricardo. Neural bearing faults classifier using inputs based on Fourier and wavelet packet transforms. Rev.fac.ing.univ. Antioquia [online]. 2013, n.67, pp.126-136. ISSN 0120-6230.

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.

Palavras-chave : Mechanical vibrations; fault diagnosis; bearings; artificial neural networks; wavelet packet transform.

        · resumo em Espanhol     · texto em Espanhol     · Espanhol ( pdf )