# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 1 Introduction  
As modern society and technology continue to evolve, the scarcity of energy has become increasingly evident. The widespread development and application of clean energy sources, including solar, geothermal, and wind power, have become a major trend in today's energy sector. In particular, the wind power industry is experiencing significant growth, with increasing wind power capacity. However, as wind turbines operate in complex and harsh environments, they face substantial challenges to the stability of their mechanical structures. The economic losses incurred by past mechanical failures indicate that components such as bearings, gears, and rotors within wind turbines are most susceptible to damage [1, 2, 3, 4]. Consequently, the ability to effectively predict and provide early warnings for wind turbine faults has become a critical issue for enhancing the reliability of wind power equipment and reducing operational and maintenance costs. Currently, fault warnings utilizing SCADA data from wind turbines have become widespread in the wind power industry. They serve as a key method to enhance equipment reliability and reduce operational and maintenance expenses. Deep learning, as an advanced technology, can automatically extract deep-level feature information from vast amounts of complex SCADA data, effectively modeling the operational status of windturbines [5; 6; 7; 8]. With this technology, the failure trend of equipment can be anticipated before or shortly after a wind turbine failure occurs, allowing for the formulation of targeted maintenance strategies in advance. This approach enhances the availability and operational efficiency of the equipment [9; 10; 11; 12]. [PERSON] et al. [13] proposed a convolutional neural network that incorporates an attention mechanism (AM) and a long short-term memory network (LSTM). This method conducts feature extraction and fault identification based on the operational state of wind turbines, significantly improving the accuracy and robustness of fault diagnosis. Furthermore, [PERSON] [14] and others introduced a wind turbine prediction method that leverages LSTM and SCADA data fusion, effectively capturing the complex dynamic changes caused by the temporal span during wind turbine operation, thereby enhancing prediction accuracy. [PERSON] et al. [15] proposed a PCABSMMR method. By combining principal component analysis, boundary line synthesis of a few oversampling techniques, and an improved multi-dimensional time series classification model, the efficient classification of abnormal and normal operation states of wind turbine blades was realized and achieved better performance than many existing methods, according to [PERSON] et al. [16]. By improving the sampling frequency of existing SCADA sensors, combined with a convolutional neural network (CNN) for multiple fault detection and classification of wind turbines, efficient online fault monitoring and status assessment were achieved. [PERSON] et al. [17] proposed a temperature-based graph neural network model, TempGNN, using SCADA data for wind turbine system-level monitoring and a decoupling model and adaptive map structure learning, effectively reducing false positives and improving the monitoring performance through actual data validation, proving that the model, in abnormal detection, has a strong effect and robustness. [PERSON] et al. [18] proposed a false data injection attack detection model based on an improved neural network (CNN-GRU). By combining the GRU structure for spatial and temporal feature extraction and using the Mississippi open-source data set verification, the model showed good accuracy in attack detection. [PERSON] et al. [19] proposed a CNN-LSTM model based on an attention mechanism. By extracting features through CNN, LSTM captures the temporal relationship and optimizes the output when combined with the attention mechanism, which further improves the efficiency, accuracy, and generalization ability of the model.