Methodology for evaluating neural networks inputs for gear fault detection

Author

Moreno, R. ; Pintado, P. ; Chicharro, J.M. ; Morales, A.L. ; Nieto, A.J.

Author_Institution

Area de Ing. Mec., E.T.S.I. Ind. (UCLM), Ciudad Real

fYear

2009

fDate

14-17 April 2009

Firstpage

1

Lastpage

6

Abstract

In this paper, the artificial neural network (ANN) inputs selection for detecting and quantifying the progressive value of an incipient defect in gears is carried out by experimental design evaluation. Several parameters in time-domain (root mean squared, crest factor, energy ratio, FM0, Kurtosis, FM4, NA4, M6A, NB4) and multiscale Hilbert-wavelet transformations are evaluated as a possible inputs. Suitable inputs, according to the proposed evaluating methodology, do enhance ANN performance.

Keywords

Hilbert transforms; condition monitoring; design of experiments; fault diagnosis; gears; neural nets; time-domain analysis; wavelet transforms; artificial neural network input selection; condition monitoring; experimental design evaluation; gear fault detection; multiscale Hilbert-wavelet transformation; time-domain analysis; Analysis of variance; Artificial neural networks; Cepstral analysis; Design for experiments; Fault detection; Gears; Maintenance; Neural networks; Torque; Wavelet transforms; Gear; detection; fault; neural network inputs;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics, 2009. ICM 2009. IEEE International Conference on

Conference_Location

Malaga

Print_ISBN

978-1-4244-4194-5

Electronic_ISBN

978-1-4244-4195-2

Type

conf

DOI

10.1109/ICMECH.2009.4957192

Filename

4957192