## 1 Introduction  
In recent years, under continuous technological developments worldwide, people's demand for energy is also growing. The increasing environmental awareness of the public has made renewable energy one of the important development projects of power supply systems [1; 2; 3]. Wind power generation has rapidly developed in many countries with good wind fields. However, offshore or onshore wind turbines are exposed to the atmosphere for a long time, and the operational environment is quite harsh. The probability of parts deterioration and machine aging increases relatively as the system running time extends. There are many challenges to wind turbine system repair and fault diagnosis. Presently, many studies concerning large wind turbine fault diagnosis aim at diagnosing faults in the gearbox and blades [4; 5; 6]. Gearbox damage has been a very important topic. The common faults of gearboxes include rust [7], stripping, wear, shaft misalignment, fracture, spalling, and aging [8; 9]. Scholars have used different methods to discuss the gearbox fault diagnosis. [PERSON] et al. measured the vibration signals of faults in gears and bearings, calculated the RMS value and peak of vibration, and used the data to analyze fault features [10]. [PERSON] et al. measured the phase current of the generator and the RMS value of vibration signals. They predicted the health index of the gearbox by a recurrent neural network (RNN) using the real-time recurrent learning (RTRL) algorithm. The predicted health index was compared with the threshold to evaluate the gearbox faults. The experimental results showed the effectiveness of the method [11]. [PERSON] et al. proposed a new method for wind turbine gearbox vibration analysis based on discrete spectrum correction. They analyzed the vibration of the stationary and planetary gears in the gearbox [12]. [PERSON] et al. built a dynamic model for double planetary gear, applied a sun gear with cracks at its root, and analyzed the gear's vibration signals. They compared the vibration signals induced by the cracks in different positions. Their method successfully identified the position of cracks [13]. [PERSON] et al. processed the measured gearbox vibration signals by filtering and Wavelet Transform. They compared the vibration signals from normal and damaged gear. The results proved the effectiveness of their method in extracting the vibration characteristics of the faulty gearbox [14]. [PERSON] et al. calculated the amplitude of fault characteristic frequency from the power spectrum density (PSD) of resampling envelope signals as the feature of gear fault detection. The feature was imported into the support vector machine (SVM) based on stacked AE (SAE) to identify the types of gear faults, such as missing teeth, cracks, and breaking. The method proposed by [PERSON] et al. was compared to the traditional SVM and the deep belief network (DBN)-deep neural network (DNN). The results of [PERSON] et al. 's research show that the recognition rate of the SAE-based SVM was 89.3%, the highest among the three classifiers [15]. [PERSON] et al. used a random Bernoulli matrix to compress the vibration signals of a high dimensional original planetary gearbox and a mixed chaotic quantum particle swarm optimization (CQPSO) algorithm with deep belief network (DBN) to extract deep fault features. The gear crack and wear were identified using the least-squares support vector machine (LSSVM) classifier; the accuracy was 98.54%. The advantage of this method is that noise pollution is eliminated from the compressed signals, and the overall amount of computation is reduced [16]. [PERSON] et al. proposed a multiview sparse filtering (MVSF) method that can extract useful and complementary features in different views from the original current signals. These features and the features extracted from wavelet packet transform (WPT), empirical mode decomposition (EMD), stacked autoencoder, and DBN were imported into the SVM for fault recognition.