# MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
## 1 Introduction
[23] introduced a fault diagnosis system tailored for cross-domain applications by integrating a deep transfer convolutional neural network with a supervised joint matching strategy. The process begins with applying continuous wavelet transform to the raw vibration signals from bearings, converting them into time-frequency representations. Subsequently, they fine-tuned a deep transfer convolutional neural network to adeptly extract complex features across different domains. To address domain adaptation challenges, the researchers developed a novel supervised joint matching method, which combines the alignment of feature distributions with instance reweighting based on the maximum margin criterion. Finally, they trained an intelligent diagnostic model capable of accurately predicting the labels of feature data in the target domain. The above methods based on transfer learning are aimed at the situation where the fault types in the source domain and the target domain are the same. However, in the actual working conditions, there are often some unknown fault types, that is to say, the fault data cannot be obtained, which will cause the fault types in the target domain that do not exist in the source domain. At the same time, there will be some fault types in the source domain that do not occur in the actual working conditions. This problem can be abstracted as the Open Set Domain Adaptation problem in transfer learning, which was originally highlighted by [PERSON] et al. [24]. This problem reflects the limitations of traditional closed set domain adaptation for image classification, and a method suitable for both closed set and open set scenarios was proposed. [PERSON] et al. [25] proposed Open Set Domain Adaptation by Backpropagation (OSDABP). [PERSON] et al. [26] improved the distance calculation method for the core problem and proposed Improved Open Set Doamin Adaptation With Backpropagation. These methods are aimed at the field of image recognition. [PERSON] et al. [27] proposed a method for OSDA problems in the field of bearing fault diagnosis, which involved a new feature separation method. The above methods only adjust the domain adaptation methods and do not propose suitable feature extraction methods for this problem. Based on the previous research, this paper studies the bearing fault diagnosis method using transfer learning to solve the problem of low accuracy in recognizing unknown fault types. The main contributions of this paper are as follows:  
1. Construction of a bearing fault diagnosis model. By constructing a fault diagnosis model of unknown type bearing based on transfer learning and defining the problem as an Open Set Domain Adaptation problem, this paper solves the problem that unknown type bearing faults cannot be classified, effectively improves the generalization ability of the model, and improves the accuracy rate of fault diagnosis. 2. Design of a network structure based on multi-scale feature extraction and an improved convolutional reconstruction module. Through the extraction of fault features of different scales and the reconstruction of bearing data feature maps, the extraction efficiency of key features is improved, and the extraction of redundant features is reduced, which ultimately improves the efficiency of transfer learning. 3. Design of an improved loss function. By improving the structure of the loss function, the performance of the proposed method is improved under the condition of data imbalance, and the Wasserstein distance is introduced to improve the efficiency of adversarial training.