Research on Bearing Fault Diagnosis Method Based on Two-Dimensional Convolutional Neural Network

Abstract

Aiming at the problem that traditional bearing fault diagnosis methods rely on artificial feature extraction and expert experience, this paper proposes an adaptive bearing fault diagnosis method based on two-dimensional convolutional neural network. In order to retain the features of the original fault data to the greatest extent, the original signal is directly used as the input, and the two-dimensional convolutional neural network fault diagnosis model is used to perform adaptive hierarchical feature extraction, and optimization algorithms are used to improve the performance of the test set. The experimental results show that this method can achieve a fault recognition rate of more than 99% on the bearing data set, and shows good generalization performance under different loads, which is feasible for practical applications.