###### Abstract
[34] combined CNN with short and long duration memory network and proposed a new convolutional short and long duration memory recurrent neural network. In addition, on that basis, some scholars generated the spectrum graph of vibration data through a time-frequency analysis and proposed a lightweight convolutional neural network to classify bearing faults [35; 36]. [PERSON] and [PERSON] [37] combined CNN and RNN. They proposed a dual-channel circulating neural network, which solved the problems of domain adaptive and high-frequency noise under actual working conditions. In addition, CNN was also used to extract the features of CWT, STFT and HHT time-frequency images, respectively [38; 39; 40]. These deep learning methods were novel, but they required more computing resources. Many hyperparameters need to be determined in advance, such as activation function, iteration number, learning rate, convolution kernel size, network layer number, etc. In summary, in the intelligent fault diagnosis of rolling bearings, many scholars only used one of the methods of frequency compression and time compression. Still, they ignored the different applicable characteristics of the two kinds of methods. The compression along the frequency axis is suitable for signal components with slowly varying frequency (SCSVF). Conversely, compression along the time axis is more suitable for signal components with rapidly varying frequency (SCRVF). Rolling bearing vibration signals collected by sensors are rich and complex, often interwoven with SCSVF and SCRVF. Therefore, the combination of MSST and TMSST can be more conducive to bearing fault diagnosis. In addition, diagnostic models are divided into traditional machine learning models and deep learning models, both of which have advantages and disadvantages. The conventional method is interpreted well, but the process is complicated and has a poor-fitting ability. Deep learning automatically extracts features, but it has a high computational cost and many hyperparameters. Both methods are favored by a large number of researchers. However, the biggest obstacle to their wide application in the field of rolling bearing fault diagnosis is still how to establish a high-precision, and high-efficiency fault diagnosis model [41; 42]. Therefore, it is crucial to integrate the information of multiple time-frequency images and diagnostic design models with more vital feature extraction ability and better performance. Meanwhile, the above methods have great advantages in the case of constant speed, but the advantages are not obvious in the case of variable speed. This paper proposes a fault diagnosis algorithm in the case of variable speed. Given the above problems, this paper proposes time-frequency compression fusion (TFCF) and residual time-frequency mixed attention network (RTFANet). Firstly, two time-frequency images obtained by TMSST and MSST are fused to transform the vibration signals into dual-channel time-frequency images. Then, the attention mechanism is introduced from three aspects of channel, time, and frequency combined with the residual connection. The model can selectively focus on essential time-frequency information, avoid information overload, and extract the practical features under the framework of the convolutional neural network to solve the problem of the weak generalization ability of the model.