Yanqiang Sun1, Hongfang Chen1,*, Liang Tang1, Shuang Zhang1
CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 1011-1028, 2019, DOI: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 More >
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