Affiliations: School of Mechanical and Electrical Engineering, Wuhan
Institute of Technology, Wuhan, Hubei, China
Note: [] Corresponding author: Hanxin Chen, School of Mechanical and
Electrical Engineering, Wuhan Institute of Technology, Wuhan, Hubei, China.
E-mail: pg01074075@163.com
Abstract: A novel intelligent method based on wavelet neural network (WNN) was
proposed to identify the gear crack degradation in gearbox in this paper. The
wavelet packet analysis (WPA) is applied to extract the fault feature of the
vibration signal, which is collected by two acceleration sensors mounted on the
gearbox along the vertical and horizontal direction. The back-propagation (BP)
algorithm is studied and applied to optimize the scale and translation
parameters of the Morlet wavelet function, the weight coefficients, threshold
values in WNN structure. Four different gear crack damage levels under three
different loads and three various motor speeds are presented to obtain the
different gear fault modes and gear crack degradation in the experimental
system. The results show the feasibility and effectiveness of the proposed
method by the identification and classification of the four gear modes and
degradation.
