Fault Diagnosis of Gearbox Based on Wavelet Transforms and Neural Networks

Guang Bin Zhang; Yun Jian Ge; Yong Jiu Liu

doi:10.4028/www.scientific.net/AMM.148-149.919

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 148-149Fault Diagnosis of Gearbox Based on Wavelet...

Fault Diagnosis of Gearbox Based on Wavelet Transforms and Neural Networks

Abstract:

In this paper, an actual system based on wavelet transform and artificial neural networks was established to diagnose different types of fault in a gearbox. As a key step, biorthogonal wavelet was used to denoise in feature extraction of signals because of its properties of compact support, high vanishing moment and symmetry. Consequently, a multi-layer perceptron network were designed to diagnose the fault status with feature vectors as inputs. In order to improve the network learning speed and stability, Levenberg-Marquardt algorithm was used to train the network. The present classification accuracy indicates the effectiveness of gearbox failure diagnosis.

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Periodical:

Applied Mechanics and Materials (Volumes 148-149)

Pages:

919-922

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.148-149.919

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Online since:

December 2011

Authors:

Guang Bin Zhang, Yun Jian Ge, Yong Jiu Liu

Keywords:

Biorthogonal Wavelet, Fault Diagnosis, Gearbox, Neural Network (NN)

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