## 1 Introduction  
Rotating machinery plays an essential role in many industries. Bearings are among the most common components for rotating machinery. The stability of automated machinery and equipment is one of the crucial factors affecting factory production. For example, mechanical vibration can cause bearing damage, spindle eccentricity of rotating machinery, and the damage and failure of equipment. Thus, exploring the precise approaches for fault diagnosis is of great value because unpredictable faults of machinery can lead to severe damage and losses in production. In fault diagnosis, most defects are resulted from equipment vibration. The vibration of bearings is often used as a means of fault diagnosis and prediction of the equipment's remaining life to improve the equipment's stability and reduce economic losses [1]. In smart manufacturing, the application of intelligent processing equipment is a trend. This can be seen in the fact that more and more manufacturers utilize electromechanical integration, online monitoring, and value-added software technologies to improve the performance of machinery. Hence, the research and application of fault diagnosis are of interdisciplinary work [2]. [PERSON] et al. [3] proposed 1D convolutional neural network fusing frequency domain feature matching algorithm (FDFM) to learn the crucial features directly from the frequency domain, and perform fault identification under limited samplesand a noisy interference environment. [PERSON] et al. [4] proposed an end-to-end fault diagnosis of rolling bearings by wavelet packet transform (WPT) and CNN methods. Generally, there are generally two approaches to fault diagnosis--data-driven methods and fault-based modeling. The traditional data-driven models of fault diagnosis rely on experts' domain knowledge about the corresponding mechanical parts. Besides, the work of exploring various machines and fault classification is labor-intensive and time-consuming [5]. The complexity of electromechanical systems building rotating machinery makes good modeling for fault diagnosis a much more challenging work. Thus, a deep learning-based data-driven model of fault diagnosis has been developed as it captures signal features automatically and rely on no professional knowledge of humans. Standard models of deep learning applied to fault diagnosis models include convolutional neural networks (CNNs), autoencoders, recurrent neural networks (RNNs), and generative adversarial networks (GANs) [6]. [PERSON] et al. [7] proposed a practical rotor failure diagnostic method with fuzzy theory and a genetic algorithm for evaluating operational status of motors. [PERSON] et al. [8] proposed a prediction method--bearing remaining useful life (RUL)--that took both time-domain features and time-frequency features into account on the basis of parallel deep residual convolution neural network (P-ResNet) to raise the prediction accuracy. [PERSON] et al. [9] proposed a residual network, which combined transfer learning (ResNet-TL) based diagnosis methods of rolling bearings, and was able to preprocess one-dimensional data of vibration signals into image data for the application of transfer learning afterwards to pre-train and re-train the ResNet34 network. [PERSON] et al. [10] proposed a text-driven fault diagnosis model based on Word2 vec, CNN, and CSM. To extract the text extraction using Word2 vec and build the prior-knowledge CNN classifier with Cloud Similarity Measurement (CSM) improved the accuracy of aircraft fault diagnosis. [PERSON] et al. [11] proposed a 1D CNN model that works on raw signals without any need of prerequisite analysis. [PERSON] and [PERSON] [12] proposed a multi-model estimation and fault detection method using RBF neural network for a nonlinear system of unknown time continuous fractional order. Testing with different operating conditions of equipment such as speed, load, environmental noise, and fault location, can result in uneven data distribution and unbalanced sampling [13].