An Interpretable CNN With Wavelet Group Policy Embedded for Intelligent Fault Diagnosis

Interpretable convolutional neural networks (CNNs) are key to reliable industrial fault diagnosis by elucidating model decision-making processes and extracting high-dimensional data features. Presently, interpretable CNNs include time-frequency transformation methods in convolutional layers, but their hyper-parameter setting (e.g. window size, overlap, type of wavelet) depends largely on expert knowledge. This paper focuses on the problem of wavelet type selection, we propose a Wavelet Shrinkage Convolutional Network (GP-WSCN) based on a group policy to solve this problem. Initially, GP-WSCN creates a pre-trained network with wavelet convolution layers and soft-threshold learning, quickly providing a basic prior for diverse wavelet waveforms. This prior knowledge is combined with reinforcement learning, allowing GP-WSCN to independently select suitable wavelet convolution kernels, reducing expert dependency. The pre-trained network is then repurposed and fine-tuned to selected kernels for swift GP-WSCN deployment in fault diagnosis tasks. Experimental results confirm the diagnostic precision and interpretability of GP-WSCN, as proven through multiple trials.