Study on bearing fault diagnosis method based on DBI-wavelet packet decomposition and improved BP neural network

Aiming at bearing fault diagnosis,taking vibration signals as study objects,a novel method based on wavelet packet decomposition（WPT） and BP neural network was proposed. Vibration signals were fed into four-layer WPT for obtaining sub-frequency bands and Davies-Bouldin index（DBI） was employed to quantitatively evaluate the results of WPT. Optimal decomposition results were produced by using FK22wavelet basis function. Improved BP neural network was used to recognize these sub-frequency band features. An elastic gradient descent method was introduced into BP neural network for alleviating the problems of slow convergence and gradient vanishing. Meanwhile,in order to determine the number of hidden layers and nodes number,orthogonal experiment was designed to verify different parameter combinations. Experiments were conducted on motor bearing and the results showed that average fault diagnosis accuracy reached as high as 98.833%.