Neural bearing faults classifier using inputs based on Fourier and wavelet packet transforms
DOI:
https://doi.org/10.17533/udea.redin.16316Keywords:
fault diagnosis, bearings, artificial neural networks, wavelet packet transform, mechanical vibrationsAbstract
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.
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