A multi convolution pooling group fault diagnosis model with high generalization across data sets and large receptive field characteristics considering industrial environmental noise

Considering the noise impact in the bearing operating environment and the time-consuming and non-universal design of traditional diagnostic algorithms, this paper proposes a new model for rolling bearing fault diagnosis, which uses convolutional pooling group (CPG) to extract features from data, At the same time, expanding the dual convolutional kernel to obtain a larger receptive field obtained the WCPGCNN (A CPG Convolutional Neural Network with Wide Convolutional Kernel as the First Lay) model based on the CPG network architecture. Firstly, the fault features of the input signal are automatically extracted through four convolutional pooling groups; Next, fault features are further extracted using the fully connected layer, and finally input into the Softmax layer for fault identification. By utilizing algorithms such as Adam, dropout, and batch normalization, the model performs well in terms of accuracy, noise resistance, and timeliness, while also possessing good cross dataset high generalization ability. This article uses the rolling bearing fault standard data from Case Western Reserve University (CWRU) and the American Society for Mechanical Fault Prevention Technology (MFPT), and verifies through multiple controlled experiments that the model established in this article has high accuracy and good generalization characteristics.