## 2 Problem Description
### Self-Supervised Model for Wind Turbine Gearboxes' Abnormal Sound Detection  
The abnormal sound detection framework of the wind turbine gearbox based on the self-supervised model is shown in Figure 3. First, the features of the gearbox sound are extracted, and the original signal is transformed into the form of time-frequency spectrum features for the next step of model training and evaluation. In the training stage, normal sounds emitted during the operation of the gearbox are used to train a self-supervised learning model to extract the latent features of normal sounds, and then the extracted latent features are used to train an abnormal score calculation module. The latent distribution of normal sound features is statistically analyzed through the probability density statistical model, and then the sound data are scored according to the latent distribution. In the testing stage, the sound to be tested obtains the hidden features through the trained self-supervised learning model, and then the corresponding abnormal score is calculated by passing this feature through the trained abnormal score calculation module. According to whether the abnormal score exceeds the threshold or not, it is indirectly judged whether the detected sound is abnormal.  
In view of the above problems, this paper proposes a self-supervised learning abnormal sound detection model for wind turbine gearboxes based on the improvement of MFN and feature fusion to improve the performance of the abnormal sound detection model under the condition that only normal data are involved in the training.  
1. This paper proposes a method that combines the Mel spectrogram feature and the time-domain feature extracted by the CNN network, effectively solving the problem of abnormal information loss that may be caused by a single feature, the Mel spectrogram feature. This feature concatenation and fusion strategy improves the accuracy of the abnormal sound detection model of the wind turbine gearbox and enhances its stability.
2. In this paper, a style attention mechanism is introduced into the MFN network to enhance the expression of features, enabling the model to capture the key features  
Figure 2: Abnormal sound detection framework.  
in the sound signal more accurately. By comparing different loss functions, the ArcFace Loss function is selected based on the experimental results. Compared with the model before improvement, the method proposed in this paper achieves significant performance improvement in the task of abnormal sound detection of wind turbine gearboxes.
3. This paper solves the problem of abnormal sound detection of wind turbine gearboxes on actual wind farms, where abnormal data are scarce and the performance of the abnormal sound detection model is poor when only normal data are used for training. In this paper, self-supervised learning is used to train the data, extract their latent features, and combine the Gaussian Mixture Model to fit the probability distribution of these features, which improves the performance of the abnormal sound detection model and provides an effective solution for the abnormal sound detection of wind turbine gearboxes.