# MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
## 3 Proposed Method
### Improved Feature Extraction Network  
In transfer learning, higher requirements are put on the feature extraction ability of deep learning models. This is because the core of methods such as the domain adaptive method in transfer learning is to minimize the difference between the distribution of features in the source domain and the target domain through adversarial domain obfuscation branching, that is, to make it as difficult as possible to distinguish between the features of the two domains, and ultimately to achieve a feature extractor that can have a better representation of both domains. In order to achieve this goal, it is necessary to extract as many effective features as possible and reduce redundant features in the feature extraction stage. The bearing vibration signal has its own characteristics. Its features include the amplitude intensity of the spectral signal, characteristic peak value, frequency band range, and other types; different layers may learn different features. At the same time, different bearings have different mechanical properties, and their feature sizes are also different. The impact of these characteristics on the feature acquisition process mainly exists in two aspects: one is that different features in the scale of the differences in the size of the convolution kernel also bring more complex requirements, and the second is that there are some differences in the importance of the signal features on different channels. Figure 4: Overall framework structure of the proposed model. Based on the above analysis, we design a feature extraction method that is more suitable for transfer learning, which mainly consists of two core ideas: one is to introduce a parallel multi-scale feature extraction structure, which enhances the extraction of features at different scales through multi-scale convolution kernels to strengthen the adaptability of features at different scales; the other is to introduce an improved convolutional reconstruction method, which reconstructs the features from the spatial and channel dimensions, to highlight important features. At the same time, we streamline the number of network layers to avoid overfitting, that is, we try to extract the common features of the bearings. Our feature extraction method is described in detail below. Our feature extraction network is based on the ResNet architecture, and its core mainly consists of a basic convolutional module and a parallel multi-branch structure including multi-scale convolutions. The basic convolutional module consists of two convolutional layers, batch normalization, ReLU activation, and AcConv modules, and includes a residual join structure. The convolution layer in the module realizes feature extraction, and the AcConv module reconstructs the extracted features to highlight the main features. Our proposed AcConv module is improved on the basis of the original ScConv module. The main improvement is that in the channel reconstruction part, the group convolution (GWC) is improved, and the convolution operation with variable convolution kernel size is modified to better adapt to different feature scales. At the same time, for the space reconstruction part, it is more important to set the feature map discrimination threshold as a learnable parameter to distinguish those features better. The multi-branch part of multi-scale convolution mainly contains four independent branches, with the four branches existing in parallel with the basic convolution module. Branch 1 uses convolution with convolution size 1 to capture simple features, Branch 2 uses convolution size 3 to focus more on moderately complex features, and branches 1 and 2 contain only one convolutional layer. Branches 3 and 4 use convolution kernels of size 5 and 7, respectively, to extract more complex features. Meanwhile, to enhance the feature extraction capability, we use two convolution kernels in branches 3 and 4, respectively, and add skip joins. The overall structure of the network is an efficient combination of two core contents.