Multi-view Bearing Fault Diagnosis Method Based on Deep Learning

Abstract

In recent years, the use of Time-frequency analysis to generate Time-frequency diagrams of vibration signals, and then the use of deep learning methods to classify them has become one of the mainstream methods for bearing fault diagnosis. However, a single Time-frequency analysis method usually cannot extract a complete vibration signal, so the accuracy of the model will be affected to a certain extent. Therefore, this paper proposes a multi-view bearing fault diagnosis method based on deep learning. The same segment of vibration signal is generated by different Time-frequency analysis methods, and then the pre-trained network is used to train the model. The Flatten layer was replaced by the Global Max Pooling (GMP) layer before the layer. The experimental results show that compared with the traditional feature fusion method, the method in this paper can not only achieve better accuracy, but also has stronger generalization.