# MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
## 1 Introduction  
In recent years, research on mechanical fault diagnosis based on computer technology has been receiving increasing attention [1, 2, 3] as the key components of machinery and equipment, bearings, and their running state directly affect the performance and reliability of the entire system [4, 5, 6]. Bearing fault diagnosis is of great significance in industrial production as it can detect potential problems in time, prevent sudden equipment shutdown, reduce maintenance costs, and enhance production efficiency. Accurate fault diagnosis can guide maintenance personnel to take targeted measures to extend the service life of the equipment and ensure production safety [7, 8, 9]. The bearing fault diagnosis approach based on computer technology is capable of achieving precise fault recognition by rapidly processing vast amounts of data and applying sophisticated algorithms. Moreover, it can minimize the impact of human factors and enhance the consistency of diagnosis. These advantages significantly elevate the level of equipment management and maintenance decision making, thereby playing a crucial role in enhancing industrial production efficiency. In recent years, deep learning, given its advantages in feature extraction, has been increasingly incorporated into the domain of bearing fault diagnosis. Relevant researchers have also put forward various enhancement approaches to enhance the feature extraction capacity by ameliorating the feature extraction network, thereby enhancing the accuracy rate of bearing fault diagnosis [10; 11]. [PERSON] et al. [12] incorporated the extended convolutional network into the domain of bearing fault diagnosis. Through augmenting the perception field of the convolution kernel and appending the global residual block, the accuracy of bearing fault diagnosis in a noisy environment was enhanced. [PERSON] et al. [13] introduced a network architecture that integrates information across multiple granular levels to effectively diagnose and locate faults in multi-bearing systems using supplementary bearing vibration data. The experiments they conducted revealed that the incorporation of multi-granularity fused information significantly boosts the system's ability to extract and identify features from intricate signals and improves its resilience to noisy environments. In addition to the above methods, [PERSON] et al. [14] developed a reinforcement learning-based methodology focused on enhancing deep features for diagnosing faults in rolling bearings. Their technique leverages the Elu activation function in conjunction with a convolutional attention mechanism, which facilitates the extraction of vital global information and strengthens the capability to enhance feature representation. [PERSON] et al. [15] put forward a bearing fault diagnosis approach based on Fault Characteristic Region (FFR) detection. The crux lies in establishing an FFR proposal network for multiple fault characteristic regions from the training samples to achieve the diagnosis of composite fault types. [PERSON] et al. [4] proposed a trusted multi-scale secondary neural network with an embedded attention mechanism for bearing fault diagnosis. By introducing secondary neurons featuring an embedded attention mechanism to optimize the convolution operation, the feature extraction capability of the standard CNN was enhanced. [PERSON] et al. [16] proposed a diagnosis method based on Convolutional Neural Network Self-feature Fusion and Transformer Data Transformation, which converts heterogeneous data into a unified format through the global self-attention mechanism of the transformer and automatically focuses on important information in different input paths. Thus, fault features can be extracted more comprehensively and accurately. Although the above methods based on deep learning have achieved satisfactory results, there are notable flaws in their application.