A fault diagnosis model for rolling bearings based on a multi-input layer convolutional neural network

Aiming at the problem that rolling bearings signal is susceptible to noise interference and the poor robustness of an intelligent diagnosis model, a fault diagnosis model for rolling bearings based on a multi-input layer convolutional neural network was proposed on the basis of a one-dimensional convolutional network. Compared with the traditional convolutional neural network diagnosis model, the model had multiple input layers. The data of initial input layer was the original signal, in order to maximize the advantages of the convolutional network to automatically learn the original signal characteristics. The spectral analysis data could be input into the network at any position of the model, in order to improve the recognition accuracy and anti-jamming ability of the model. Firstly, through a simulation test of rolling bearing, the feasibility and validity of the proposed method were verified. Then, the robustness of the model was tested by adding noise to the test set, and the recognition performance of the model in strong noise environment was improved by using an incremental learning method. Finally, through the example of rolling bearing fault, the recognition performance and generalization ability of the model were verified. Experimental results show that the proposed model can improve the recognition rate and convergence performance of the traditional convolutional model, and has good robustness and generalization ability. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.