Braking Noise Classification Based on Wavelet Scattering Deep Sequential Neural Network

Abstract:

A data driven classification method based on wavelet scattering collaborative deep sequential neural network is studied to achieve intelligent braking noise recognition. Wavelet scattering transform is used to extract the features of caliper vibration signals relating to braking noise， which form the multidimensional wavelet scattering feature vectors. These feature vectors are input respectively to a standard one layer 1D convolutional neural network （1DCNN） and a one layer bi-directional long short-term memory （BiLSTM） network. The testing results show that the accuracy for braking noise classification can be improved significantly by both networks input with multidimension features from wavelet scattering transform compared with one-dimension features from Shot-time Energy and Shot-time mean zero crossing rate. Modified 4-layer deep 1DCNN and 3-layer deep BiLSTM network are further proposed to reduce training underfitting from standard one layer networks to enhance the capability of feature capture and further improved the classification accuracy. According to F₁ indicator the 4-layer deep 1DCNN shows a better overall performance than that of 3-layer deep BiLSTM network， which also has advantages of fewer training parameters.