Open Access

ARTICLE

Gear Fault Detection Analysis Method Based on Fractional Wavelet Transform and Back Propagation Neural Network

Yanqiang Sun¹, Hongfang Chen^1,*, Liang Tang¹, Shuang Zhang¹

1 Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing, 100124, China

* Corresponding Author: Hongfang Chen. Email: .

Computer Modeling in Engineering & Sciences 2019, 121(3), 1011-1028. https://doi.org/10.32604/cmes.2019.07950

Abstract

A gear fault detection analysis method based on Fractional Wavelet Transform (FRWT) and Back Propagation Neural Network (BPNN) is proposed. Taking the changing order as the variable, the optimal order of gear vibration signals is determined by discrete fractional Fourier transform. Under the optimal order, the fractional wavelet transform is applied to eliminate noise from gear vibration signals. In this way, useful components of vibration signals can be successfully separated from background noise. Then, a set of feature vectors obtained by calculating the characteristic parameters for the de-noised signals are used to characterize the gear vibration features. Finally, the feature vectors are divided into two groups, including training samples and testing samples, which are input into the BPNN for learning and classification. Experimental results showed that this gear fault detection analysis method could well maintain the useful signal components related to gear faults and effectively extract the weak fault feature. The accuracy rate reached 96.67% in the identification of the type of gear fault.

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