# Multi-Scale Rolling Bearing Fault Diagnosis Method Based on Transfer Learning
## 1 Introduction
[22] proposed an adversarial domain adaptation network based on contrastive learning, offering a solution for bearing fault diagnosis under different conditions and reducing misclassification. [PERSON] et al. [23] introduced a generative convolutional neural network, using multi-kernel MMD to enhance the feature transferability. [PERSON] et al. [24] constructed a multi-scale deep intra-class adaptation network and developed a multi-scale feature learner, validating the model's effectiveness through extensive comparative experiments. [PERSON] et al. [25] proposed a method based on transfer convolutional neural networks and thermal imaging technology for the intelligent diagnosis of rotating bearing systems under different working conditions, overcoming training issues in traditional CNNs by introducing random pooling and activation functions and adapting the source domain parameters to the target domain through transfer learning. Although the aforementioned transfer learning and deep learning methods for bearing fault diagnosis have shown excellent performance under specific conditions, they typically rely on a key assumption: all fault types in the target domain are represented in the source domain, meaning that the source domain data cover all fault types in the target domain. However, in actual industrial applications, especially considering the safety of equipment operation, it is difficult to operate bearings under all potential fault states to collect data, leading to a lack of fault data for all types. Therefore, the fault type matching relationship between the source and target domains can actually be divided into four different scenarios, as shown in Figure 1. Figure 1 indicates that the fault categories in the source and target domains are the same, but the operating conditions may not be identical; Figure 1 shows that the target domain's fault types are a subset of those in the source domain. These two scenarios are ideal but difficult to meet in real-world settings. Figure 1 implies that the fault categories from the source domain fall within the range of the target domain's, meaning that there are additional fault types in the target domain not present in the source domain. Figure 1d shows that the source and target domains have both the same and specific categories. Clearly, the scenarios in Figure 1c,d better represent the most common scenarios in practical industrial applications. Recent research has made significant progress in addressing the issue of effectively recognizing new fault types that are limited by the constraints of the source domain. [PERSON] et al. (2016) proposed a bearing fault diagnosis method based on a dual adversarial network, incorporating an auxiliary domain discriminator to assess the similarity of individual target samples. [PERSON] et al. (2016) examined the problem of open-set fault diagnosis and proposed the BWAN model for COSFD, which assigns greater weight to shared classes during feature alignment and less weight to outlier classes. [PERSON] et al. (2017) introduced a novel OSFD method, which relaxes the constraint of identical label spaces between the source and target domains, designs a regularization method to convert open sets into pseudo-closed sets, and introduces a sample-level weighting mechanism, demonstrating the effectiveness of this fault diagnosis approach through experiments. These methods have enabled notable progress in addressing the issue of recognizing new fault types in target tasks that are limited by the constraints in source task data. They offer valuable research directions for this issue. However, there remains potential for additional enhancements in recognition accuracy. Building on the aforementioned studies, this paper presents research on bearing fault diagnosis methods based on transfer learning, aiming to further explore and improve the issue of low recognition accuracy for new fault types in target tasks due to limitations in source task data. The key contributions of this paper can be summarized as follows. 1.