## 1 Introduction  
Most of the fault diagnosis of rolling bearings is based on vibration signals and acoustic emission signals. However, the acquisition of both signals is susceptible to the influence of the external environment; i.e., noise is easily collected. Therefore, it is very difficult to extract useful information [1; 2; 3; 4]. In recent years, temperature signals have gradually received the attention of researchers. Infrared thermal imaging cameras can acquire infrared images and thus display the thermal characteristics of objects. Compared with the two signals discussed above, the temperature signal has the characteristics of non-contact, high sensitivity and real-time. Due to its strong advantages for test targets running at high speeds, some scholars have introduced it into the troubleshooting of mechanical equipment [5; 6]. Therefore, using infrared images as a research object for rolling bearing fault diagnosis has certain advantages. Regardless of the signal base, the essential series of processes for fault diagnosis consists of feature extraction, feature selection and fault diagnosis. There is much related research on traditional vibration signals and acoustic emissions. [PERSON] et al. extracted features from bearing vibration signals in the time and frequency domains and applied Multidimensional Scaling (MDS) for feature selection and finally obtained highly accurate fault diagnosis results by fusing a support vector machine (SVM) [7]. [PERSON] et al. combined the 1 Dimension Convolutional Neural Network (1 DCNN) and Long Short-Term Memory (LSTM) to avoid the errors caused by relying on expert experience and incomplete information in traditional feature extraction methods [8]. [PERSON] et al. proposed a Multi-domain Entropy-random Forest (MDERF) method that fuses multi-domain entropy and random forest to extract the multi-domain entropy of the acoustic emission signal as a feature extraction method to extract the four entropies of the AE signal and used the random forest to complete the fault diagnosis [9]. [PERSON] et al. two-dimensionally verified the diagnostic capability of the improved generative adversarial network (GAN) under unbalanced acoustic emission signals [10]. Motor Current Signature Analysis (MCSA) is widely used for motor fault diagnosis. [PERSON] et al. obtained the current signal of induction motor by MCSA and used the Continuous Wavelet Transform (CWT) model of the squirrel-cage induction motor for broken bar diagnosis [11]. [PERSON] et al. obtained the vibration signal and current signal of the motor, calculating the Power Spectral Density (PSD) and Fast Fourier Transform (FFT). The amplitudes at the frequencies of interest given by the theory were analyzed to provide reliable fault diagnosis results [12]. Usually, feature extraction from IR images and feature selection are the top priority to ensure the completion of the subsequent work. Currently, most research scholars use the method of manually extracting the grayscale features, extracting texture features of the image or parsing the image using image analysis methods. [PERSON] et al. extracted texture features, moment features and modified entropy features for server fault diagnosis [13]. [PERSON] et al. used a two-dimensional empirical mode decomposition (BEMD) which was used to enhance the infrared thermal image of the bearing, after which the second-order statistical texture features of the image were extracted, and feature selection was performed using Minimum Redundancy Maximum Relevance (MRMR) [14]. In addition, another feature extraction method is to train and learn the data using deep neural network models to obtain high-level feature representation in the data. This feature extraction method has better performance and generalization capability than the traditional manual feature extraction method. [PERSON] et al. directly extracted the features of the image using the 2 Dimension Convolutional Neural Network (2 DCNN) and showed significantly better diagnostic results compared to others [15]. [PERSON] et al.