###### Abstract  
The traditional rolling bearing diagnosis algorithms have problems such as insufficient information on time-frequency images and poor feature extraction ability of the diagnosis model. These problems limit the improvement of diagnosis performance. In this article, the input of the time-frequency image and intelligent diagnosis algorithms are optimized. Firstly, the characteristics of two advanced time-frequency analysis algorithms are deeply analyzed, i.e., multisynchrosqueezing transform (MSST) and time-reassigned multisynchrosqueezing transform (TMSST). Then, we propose time-frequency compression fusion (TFCF) and a residual time-frequency mixed attention network (RTFANet). Among them, TFCF superposes and splices two time-frequency images to form dual-channel images, which can fully play the characteristics of multi-channel feature fusion of the convolutional kernel in the convolutional neural network. RTFANet assigns attention weight to the channels, time and frequency of time-frequency images, making the model pay attention to crucial time-frequency information. Meanwhile, the residual connection is introduced in the process of attention weight distribution to reduce the information loss of feature mapping. Experimental results show that the method converges after seven epochs, with a fast convergence rate and a recognition rate of 99.86%. Compared with other methods, the proposed method has better robustness and precision. rolling bearing; time-frequency compression fusion; intelligent fault diagnosis +
Footnote †: journal:  
0000-0001-9998-3100-9  
through wavelet packet transform (WPT) and formed the time-frequency characteristic matrix of the signals. Secondly, multi-weight singular value decomposition (MWSVD) was constructed using singular value contribution rate and entropy weight to extract further the characteristics of the time-frequency characteristic matrix obtained by WPT. Finally, the extracted feature matrix is used as the input of the support vector machine (SVM) classifier for bearing fault diagnosis. [PERSON] et al. [6] used refined composite multiscale fuzzy entropy (RCMFE) for cross-domain diagnosis of bearing faults and used self-organizing fuzzy (SOF) classifier for classification. Although these methods are easy to implement, the limitations of [PERSON]'s uncertainty principle [7] prevent them from improving time and frequency resolution. To obtain time-frequency images of vibration signals with better energy concentration, [PERSON] et al. [8] suggested the synchronous squeeze wavelet transform (SSWT). In essence, it is a time-frequency analysis method of energy rearrangement. Based on CWT, spectral energy is redistributed and concentrated at instantaneous frequencies [9]. Based on this idea, [PERSON] et al. [10] proposed the synchrosqueezing S transform (SSST). [PERSON] et al. proposed the Multisynchrosqueezing Transform (MSST) [11] and Time-reassigned Multisynchrosqueezing transform (TMSST) [12]. They performed multiple iterations based on synchronous compression transformation. They proved that the error between the time-frequency representation obtained and the ideal case becomes smaller with the increase of iterations. In other words, this method can theoretically approach the ideal time-frequency representation infinitely, which makes it widely used in the field of bearing fault diagnosis [13; 14; 15; 16; 17; 18]. With the development of artificial intelligence, many scholars inducted deep learning into fault diagnosis.In bearing fault diagnosis, Major deep learning networks include autoencoder [19; 20; 21; 22; 23], Convolutional Neural Networks (CNN), generative adversarial network [24; 25; 26], Recurrent Neural Networks (RNN) and deep transfer learning [27; 28; 29]. Considering the characteristics of CNN and RNN, more and more scholars have applied them to rolling bearing fault diagnosis. The original one-dimensional vibration signal was collected as input and feature information was extracted adaptively through CNN [30; 31; 32; 33]. However, [PERSON] et al.