@Article{cmes.2019.07950,
AUTHOR = {Yanqiang Sun, Hongfang Chen, Liang Tang, Shuang Zhang},
TITLE = {Gear Fault Detection Analysis Method Based on Fractional Wavelet Transform and Back Propagation Neural Network},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {121},
YEAR = {2019},
NUMBER = {3},
PAGES = {1011--1028},
URL = {http://www.techscience.com/CMES/v121n3/38079},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2019.07950}
}