# Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
## 1 Introduction  
Due to the advantages of it being clean, efficient, easy to deploy, and cost-effective, wind energy has become one of the primary technologies for energy generation in most countries around the world [1]. However, with the increase in installed capacity, the daily maintenance, defect detection, and fault prevention of wind turbines have become more urgent and important. Due to factors such as remote deployment locations, high altitude, and severe climate change [2; 3], the daily maintenance and fault detection of wind turbines is often more challenging than other traditional power generation technologies. Among the complex structures of wind turbines, the gearbox is often one of the components with the highest failure rates. Moreover, the gearbox is usually installed at the top of a tower, 60 to 70 m or even higher, and the internal space of the wind turbine is very narrow. Once a fault occurs, maintenance is difficult to perform. These unfavorable factors pose challenges and difficulties to gearbox fault detection and also become a huge obstacle to wind turbine fault prevention [4; 5]. The main faults in gear operation include gear wear and aging, tooth surface adhesion and abrasion, tooth surface contact fatigue, and tooth breakage. Among the common components of gearbox failures, abnormal bearing status often accounts for a large proportion. Therefore, gearbox fault diagnosis is mainly aimed at bearing fault diagnosis and prevention. In the early days, the processing and analysis of bearing signals weremainly achieved through the classical fast Fourier transform (FFT) [6; 7]. However, FFT has shortcomings, such as low component resolution, spectral distortion, and non-smooth peak signals. To solve the shortcomings of the FFT method, researchers have proposed methods such as maximum entropy estimation [8], autoregressive spectrum analysis [9], and wavelet analysis [10]. Although these methods have improved the fault detection rate of FFT, when gear faults occur the observed vibration signals are often non-stationary and contain significant noise interference. The robustness of these methods is often low, making it difficult to apply them in the complex deployment environment of wind turbines. Recently, with the high parallelism, associative memory, efficient representation, and high fault tolerance of deep learning technology, gear fault diagnosis methods based on deep learning have emerged. The goal is to efficiently decouple and model the interrelated, high-dimensional, and noisy signal features, to identify the operating state of the gearbox from complex bearing status signals, and to achieve high-precision fault identification and prediction. In [11] the gearbox signal was first transformed into a time-frequency spectrogram using wavelet transform, and then a fault health classification model based on a convolution neural network (CNN) was proposed. Meanwhile, [12] designed a Bayesian state classifier based on CNNs and applied it to planetary gearbox fault detection. [PERSON]. [13] collected a large-scale gearbox bearing wear signal dataset and proposed a neural fuzzy prediction model based on Mamdani compositional inference. Although neural networks have strong feature extraction and signal analysis capabilities, gear signals are actually typical non-stationary and time-varying time series data, and CNNs often perform well on data types with significant spatial structures, such as images and videos, but cannot effectively model the time correlation of gear signal data [14]. [PERSON]. [15] studied a deep model based on empirical mode decomposition (EMD) and recurrent neural networks (RNNs) and applied it to the state monitoring and fault diagnosis of wind turbine bearings, achieving some performance improvement.