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Rolling bearing fault diagnosis based on continuous wavelet transform and transfer convolutional neural network

Rolling bearing is an important part of rotating machinery. On-line condition monitoring and fault diagnosis of rolling bearing are of great significance to ensure the stable operation of rotating machinery. In this paper, a novel rolling bearing fault diagnosis method is proposed based on continuous wavelet transform (CWT) and transfer convolutional neural network. Firstly, the vibration signal is converted into time-frequency image through CWT. Then, on the basis of the pre-trained convolutional neural network model, the construction of rolling bearing fault diagnosis model is conducted based on transfer learning. Moreover, an open source data set obtained from the Bearing Data Center of Case Western Reserve University was used to verify the effectiveness of the proposed method. Finally, the comparative analyses were conducted among the proposed method and other rolling bearing fault diagnosis method. The results show that the proposed method can achieve higher accuracy on the accurate rolling bearing diagnosis of fault modes and fault degrees.