## 1 Introduction  
Modern machinery is evolving in the direction of high performance, high precision, and intelligence as technology and industrialization continue to improve. Particularly rolling bearings are becoming more crucial in production, transportation on rails, and other areas [1]. As the primary rotating equipment component, prognostics health management (PHM) is a necessity to prevent significant financial losses and human tragedies in businesses [2]. Therefore, fault diagnosis and feature extraction, two essential PHM processes, have become important research topics in practical industrial settings. The \"big data\" era has now arrived in the field of machinery health monitoring, and some of the drawbacks of earlier fault diagnosis methods have been effectively addressed [3]. Utilizing a data-driven approach will make troubleshooting intricate mechanical systems less challenging [4]. Data-driven approaches have gone through two stages: Traditional Intelligent Fault Diagnosis (TIFD) based on manual features and Modern Intelligent Fault Diagnosis (MIFD) based on deep learning. TIFD first requires feature extraction from the signal and then a classification algorithm. Frequently employed methods include Spectral Clustering (SC) [5], K-Nearest Neighbor (KNN) [6], Extreme Learning Machine (ELM) [7], and Support Vector Machine (SVM) [8]. These methods' features, however, have a poor ability to generalize; they are unable to directly identify features on the original test data. Deep Learning (DL) has overcome these drawbacks and evolved into a potent intelligent diagnosis approach [9, 10]. Different DL methods, including Restricted Boltzmann machine (RBM) [11], Auto-Encoder (AE) [12], Recurrent Neural Networks (RNN) [13], Convolutional Neural Networks (CNN) [14], etc., are utilized to tackle various diagnostic problems. An integrated depth auto-encoder was effectively employed by [PERSON] et al. [15] in feature extraction and intelligent fault diagnosis of bearings. [PERSON] et al. [16] suggesteda novel convolutional long-term memory recurrent neural network for fault detection. However, traditional DL methods demand a huge quantity of high-quality labeled data, as a limited sample size will cause overfitting and impair performance. It is common knowledge that equipment is not permitted to run in a fault status in real industrial settings. Therefore, it is very challenging to gather enough marker data for each failure type. Few-shot learning has significantly improved recently at overcoming the data scarcity issue, especially in image classification [17; 18]. Common model, Transfer Learning, and Few-shot learning are depicted in Figure 1 with various colors denoting various domains. Common models assume that there are enough samples, and that the train set and test set are in the same domain. Transfer Learning refers to the train set and test set being in different domains, with correlation between the domains. Fewer samples are provided in the few-shot learning training set compared to classic deep learning and transfer learning, which makes it easier to tackle issues such as conditional transfer and few-shot scenes. In order to diagnose bearings in the presence of sparse fault data, [PERSON] et al. [19] introduced a novel few-shot learning diagnosis. [PERSON] et al. [20] proposed a small sample denoising fault diagnosis algorithm, which can solve the bearing fault diagnosis problem under small samples. [PERSON] et al. [21] proposed a me-ta-learning fault diagnosis method. It attempts to obtain prior knowledge by optimizing initialization parameters. Fast and accurate bearing fault diagnosis is realized under unknown operating conditions using a small number of samples. [PERSON] et al. [22] proposed a new diagnosis method based on less shot learning, which is applicable to the environment of data scarcity. The above methods require the source domain dataset to be pre-trained, and the correlation between the source domain and the target domain significantly affects the results.