# MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
## 1 Introduction
Firstly, the training data and test data are required to adhere to the same distribution, meaning that all types and all conditions of load data need to be encompassed in the training data to achieve accurate fault diagnosis. Nevertheless, this is challenging to accomplish under actual working conditions, so researchers have commenced attempts to achieve the diagnosis of unknown working conditions and unknown types of faults through other approaches. Researchers have widely adopted transfer learning in the realm of bearing fault diagnosis due to its effective cross-domain adaptation capabilities. This approach involves segregating data into two distinct categories: the source domain and the target domain. In the context of our study on bearing fault diagnosis, the source domain comprises data that are accessible for training models, while the target domain includes data that must be analyzed and diagnosed under real-world conditions. [PERSON] et al. [17] conducted supervised classification on source domain data and implemented batch nuclear norm maximization on target domain data to mitigate label offset effects. [PERSON] et al. [18] proposed a new adversarial transmission network guided by class aggregation (ATN-CA) for the state diagnosis of bearings with small samples. The introduced local difference feature representation enables ATN-CA to concentrate on the distinctive characteristics of bearings, preventing a single neural network from overlooking crucial fault information. Additionally, the proposed class aggregation guidance strategy leverages the semantic information from the signals to dynamically adjust both the marginal and conditional distributions of the source and target datasets. This adjustment effectively reduces the distribution gap for the same class across different domains, thereby facilitating accurate transfer diagnosis. [PERSON] et al. [19] developed a deep transfer learning model that integrates multi-domain information fusion with Multi-kernel Maximum Mean Difference (MK-MMD). Their approach includes constructing a multi-domain feature extractor using a multi-attention mechanism and performing feature integration through a Bidirectional Gated Recurrent Unit (BiGRU) network. The MK-MMD component is employed to minimize the distribution gap between the source and target domains, thereby achieving domain-invariant feature representations. The aforementioned methods have achieved good results by incorporating transfer learning techniques into fault diagnosis. At the same time, some researchers have also proposed fault diagnosis methods based on transfer learning. [PERSON] et al. [20] incorporated the finite element simulation data of bearing faults into the domain adversarial network and used the fault evolution mechanism to guide the adversarial training. The proposed algorithm establishes both global and subdomain classifiers, substituting the label predictions for target domain data within each subdomain with simulated signals to minimize information transfer. By redesigning the loss function, it effectively extracts shared features of identical bearing types across various operating conditions, thereby enhancing robustness against domain adversarial challenges. [PERSON] et al. [21] used the VGG16 model based on transfer learning as the feature extractor and the random forest as the classifier to realize the adaptation to different working environments and enhance the fault classification performance under different working conditions. [PERSON] et al. [22] introduced the Dual-Domain Signal Cooperative Transmission Network (DSCTN) for diagnosing bearing faults across different domains under variable speed conditions. The network is built on the Swin Transformer architecture and utilizes dual-domain signals. They developed a multi-level fusion feature extraction module that effectively captures essential features from both time and frequency domains, thereby diminishing the effects of speed fluctuations. [PERSON] et al.