Rolling bearing fault diagnosis method based on dual noise reduction

The model feature extraction is enhanced by suppressing noise interference and optimizing feature sensitivity to improve its robustness in practical applications. A fault diagnosis method for rolling bearings based on convolutional neural networks in a strong noise environment. Experiments show that this model demonstrates high robustness and generalization ability under noisy conditions. It provides a novel framework for industrial fault diagnosis to solve the problem of fault signals being submerged by noise.