###### Abstract
Abnormal sound detection first extracts hidden features through a deep learning model, and then the abnormal score calculation module calculates the abnormal score of the sound to be tested. If it exceeds the threshold, it is judged as abnormal [12; 13]. Early studies used manual feature extraction methods such as MFCC as input and probability models such as Gaussian Mixture Models and Support Vector Machines to calculate abnormal scores [14; 15; 16; 17]. However, these methods may have information loss during the extraction process, and the feature extraction is complex and relies on a manual design, with disadvantages such as poor generalization ability and detection performance. In recent years, some studies have used methods such as AutoEncoders (AEs), Variational AutoEncoders (VAEs), Sparse AutoEncoders (SAEs), and Adversarial AutoEncoders (AAEs) to represent certain inherent laws of normal data by learning low-dimensional features [12; 18; 19; 20]. When abnormal samples are input, a greater reconstruction error is generated, and the reconstruction error is used as a means to detect abnormal scores. Since  
Figure 1: Newly installed capacity of wind turbine generators from 2019 to 2023.  
the model is trained only with normal sound data, if the abnormal sound to be tested also conforms to the characteristics of the normal sound training, the effectiveness of this model may be limited. To better model normal sounds, the literature [21] uses a self-supervised classification method, and its performance is superior to the unsupervised method based on autoencoders. However, this method lacks certain stability. In the abnormal sound detection of gearboxes, the detection results at different measuring point positions vary greatly. To solve this problem, the literature [22; 23; 24] proposed Generative Flow (Glow) and Masked Autoregressive Flow (MAF). This method improves the performance of the abnormal sound detection model, but its stability is still limited. In order to develop a stable and effective method for detecting abnormal sounds in wind turbine gearboxes, the effective features that can be used for the task of abnormal sound detection must be studied. The Mel spectrogram is widely used in abnormal sound detection. By simulating the human ear's perception of frequency, a Mel filter bank is used to capture different frequency information [25]. However, this mechanism may filter out the high-frequency components in the spectrum, where the distinct features of abnormal sounds may exist. Therefore, the Mel spectrogram feature may not be able to completely distinguish between normal and abnormal sounds, and when used together with self-supervised methods, it can lead to unstable performance. A method STgram that combines the Mel spectrogram (Sgram) and time-domain features (Tgram) has been used, making the input feature information more comprehensive [26]. To extract the feature information of the spectrogram more effectively, by adding a style attention mechanism in MFN, the feature extraction network can better extract the style features of the sound spectrogram. However, when detecting completely unknown abnormal samples, it is challenging to classify highly similar samples and determine the decision boundary, resulting in poor performance. In view of the above problems, this paper proposes a self-supervised abnormal sound detection model based on the fusion of the Mel spectrogram and time-domain feature information, the improvement of MFN, and the use of ArcFace Loss as the loss function. In view of the above problems, this paper proposes a self-supervised wind turbine gearbox sound anomaly detection algorithm that fuses time-domain features and Mel spectrograms, extracts hidden features by introducing SRM in the MFN network, uses ArcFace Loss to train the classifier, further trains the extracted features to obtain the GMM, and determines the machine operating status based on the score of the test data.