Abstract：Aiming at the problem that rolling bearings under variable operating conditions cannot obtain a large number of labeled sample data and the low recognition rate of traditional deep learning diagnostic methods, a convolutional neural network rolling bearing fault diagnosis method based on transfer learning is proposed. First, the short-time Fourier transform is used to process the vibration signal of the rolling bearing to obtain the source domain and target domain sample sets; second, the source domain samples are used to pre-train the convolutional neural network model; finally, the target domain samples are used to fine-tune the convolutional neural network model to implement the rolling bearing troubleshooting.Two different rolling bearing vibration data are used to carry out migration fault diagnosis experiments.The experimental results show that: compared with the fault diagnosis method of convolutional neural network, the fault diagnosis recognition rate of convolutional neural network based on transfer learning is increased by 7%.
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2022, Vol. 43 
