Research on Fault Diagnosis and Feature Extraction Mechanism of Rotating Machinery Based on WGS-CNN

Aiming at the problem of fault diagnosis of rotating machinery under complex working conditions, a convolutional neural network (CNN) model with an improved convolutional layer based on wavelet transform (WT) is proposed. First, multiscale wavelets are employed to initialize the convolutional kernels, endowing the model with strong feature extraction capabilities and enhancing its multiresolution properties. Subsequently, multiple adaptive Gaussian windows (GWs) are introduced. These windows allow the convolutional kernels to concentrate on the central frequencies of faults by utilizing learnable gradients, effectively suppressing noise interference. Finally, the square function is used to suppress low-amplitude frequency components across the entire frequency band while enhancing the main frequency characteristics beneficial for classification. Through fault diagnosis experiments on the public datasets, the antinoise performance of the model stably reaches over 90%, significantly outperforming similar models. In-depth mechanism analysis reveals how each improvement strategy operates and verifies the effectiveness of incorporating wavelets, GWs, and a square function into the CNN.