Convolutional neural network (CNN), with ability of feature learning and
nonlinear mapping, has demonstrated its effectiveness in prognostics and health
management (PHM). However, explanation on the physical meaning of a CNN
architecture has rarely been studied. In this paper, a novel wavelet driven
deep neural network termed as WaveletKernelNet (WKN) is presented, where a
continuous wavelet convolutional (CWConv) layer is designed to replace the
first convolutional layer of the standard CNN. This enables the first CWConv
layer to discover more meaningful filters. Furthermore, only the scale
parameter and translation parameter are directly learned from raw data at this
CWConv layer. This provides a very effective way to obtain a customized filter
bank, specifically tuned for extracting defect-related impact component
embedded in the vibration signal. In addition, three experimental verification
using data from laboratory environment are carried out to verify effectiveness
of the proposed method for mechanical fault diagnosis. The results show the
importance of the designed CWConv layer and the output of CWConv layer is
interpretable. Besides, it is found that WKN has fewer parameters, higher fault
classification accuracy and faster convergence speed than standard CNN