부산대학교 도서관

Aiming at the problem of large amount of calculation for the time-frequency distribution of mechanical vibration signals with good representation ability, a mechanical fault diagnosis method based on dual-tree complex wavelet packet time-frequency distribution and residual network transfer learning is proposed. Firstly, the high-frequency and low-frequency components of the signal are decomposed based on the dual-tree complex wavelet packet with translation invariance, and the discrete time-frequency distribution of the mechanical vibration signal is efficiently obtained. In view of the fact that ResNet uses residual modules and cross-layer connections to build a deep network structure, it has strong image recognition and classification capabilities, Residual network transfer learning is used to train and classify the obtained time-frequency distribution images, and the Western Reserve University bearing dataset and the engines measured signal are used to verify the method proposed in this paper, and it can obtain ideal fault diagnosis results under different input images sizes and a small number of training sample conditions.