###### Abstract  
To solve problems such as the unstable detection performance of the sound anomaly detection of wind turbine gearboxes when only normal data are used for training, and the poor detection performance caused by the poor classification of samples with high similarity, this paper proposes a self-supervised wind turbine gearbox sound anomaly detection algorithm that fuses time-domain features and Mel spectrograms, improves the MobileFaceNet (MFN) model, and combines the Gaussian Mixture Model (GMM). This method compensates for the abnormal information lost in Mel spectrogram features through feature fusion and introduces a style attention mechanism (SRM) in MFN to enhance the expression of features, improving the accuracy and stability of the abnormal sound detection model. For the wind turbine gearbox sound dataset of a certain wind farm in Guangzhou, the average AUC of the sound data at five measuring point positions of the wind turbine gearbox using the method proposed in this paper, STgram-MFN-SRM, reached 96.16%. Compared with the traditional anomaly detection methods LogMel-MFN, STgram-MFN, STgram-Resnet50, and STgram-MFN-SRM(CE), the average AUC of sound detection at the five measuring point positions increased by 5.19%, 4.73%, 11.06%, and 2.88%, respectively. Therefore, the method proposed in this paper effectively improves the performance of the sound anomaly detection model of wind turbine gearboxes and has important engineering value for the healthy operation and maintenance of wind turbines. anomalous sound detection; feature fusion; self-supervised learning; MobileFaceNet; Gaussian mixture model +
Footnote †: journal: Applied Sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00  
reached 1021 GW. However, while the wind power industry has achieved unprecedented development, the number of faults in the wind turbine drive train has also increased sharply, leading to an increase in the maintenance costs of wind turbines, which can account for about 20% to 30% of the total cost [6]. According to data statistics, gearbox faults cause the longest downtime of wind turbines. To reduce the downtime, measures such as condition monitoring and regular maintenance need to be taken for them. Damage to gearboxes mainly occurs on bearings and gears. The reasons include manufacturing and installation faults, misalignment, torque overload, wear, and fatigue [7]. If a defect in a gear or bearing is not identified and replaced in time, it can easily cause damage to the entire gearbox, requiring cranes, replacement, and downtime losses, resulting in even greater losses. Therefore, using an effective anomaly detection method to timely and accurately detect whether faults have occurred in the wind power generation gearbox and conduct timely maintenance to prevent larger accidents and improve production efficiency has significant engineering value. On wind farms, abnormal events rarely occur, but there are many types of abnormalities. Abnormal sound data are diverse and scarce, and collecting complete abnormal sound data is a difficult task. Therefore, in the monitoring of the operating status of wind turbine units, normal sound data are often used to train the model, and the normal and abnormal sounds to be tested are distinguished based on the characteristics of the normal sound data. In recent years, the demand for abnormal sound detection methods in the field of modern manufacturing has been increasing day by day [8]. Additionally, methods such as machine learning, deep learning, and transfer learning have become increasingly mature in the application of industrial equipment fault detection [9; 10; 11]. Researchers in multiple fields have shown that using the sound characteristics of machines in normal operation to detect unknown faults can significantly reduce the risk of industrial accidents and improve production efficiency.