Bearing Fault Diagnosis Based on Improved Morlet Wavelet Transform and Shallow Residual Neural Network

To solve the problem of the Morlet wavelet transform not satisfying wavelet admissibility conditions, which results in fuzzy and aliasing time–frequency image features, this paper proposes an improved Morlet wavelet. The scale parameter is the wavelet’s dominant frequency parameter. It largely satisfies the admissibility conditions and effectively improves the fuzzy aliasing effect of time–frequency image features. To further improve feature learning, this paper proposes a combined Morlet wavelet and residual network. The experimental results show that the accuracy of the proposed method is 10%, 4.5%, and 3% higher than that of LeNets, CNNs, and ResNets under varying load and noise disturbance levels, respectively.