Rolling bearing fault diagnosis with combined convolutional neural networks and support vector machine

For small sample data, it is difficult to complete the requirements of training complex models in the field of fault diagnosis. To solve the problem, this paper combines convolutional neural network's excellent feature processing ability with the excellent generalization ability of Support Vector Machine (SVM). The proposed CNN-SVM system is applied in bearing fault diagnosis, which takes the time domain diagram of bearing vibration data as the system input. The features are extracted by CNN, and realizes the final bearing state recognition by SVM. The contribution of the paper is to add three conditions for automatically switch CNN to SVM. The results show that the system has the advantages of less time-consuming, high precision and strong generalization ability. Experimental results show that the time consumption of this model is 1/3 of CNN, and the accuracy of the training set and the testing set are 100% and 99.44%.

Automated summary

This paper combines CNN and SVM for bearing fault diagnosis, improving the model's generalization ability and accuracy. Experimental results show the system has advantages of less time-consuming, high accuracy, and strong generalization ability.