# Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features
## 1 Introduction  
With the rapid development of wind power, a lot of wind turbines have faults in the operation because most of them are installed in remote areas, and their loads are unstable. The operation and maintenance cost of up to 25-30% seriously restrict the development of wind power industry [1; 2]. Therefore, how to reduce the risk of wind turbine operation and decrease the cost of wind power generation has become a research topic for many scholars. The fault isolation of wind turbine is the bottleneck to solve the above problems; undoubtedly, it is the key to ensuring long-term stable operation of the wind turbine, and it is the necessary means to realize the condition-based maintenance of the wind turbines [3]. Variable speed constant frequency doubly fed wind turbine is the main type in wind farms [4], and its simplified model is shown in Figure 1. The fault rate of gearbox and generator is relatively high [5]. There are many reasons for the faults of gearbox and generator, and the misalignment between them is one of the most important [6]. This is mostly because:  
1. The precise alignment in the wind turbine is very difficult;
2. The wind speed fluctuation can cause the start-stop of wind turbine frequently, and, as time goes on, it will cause the shift of generator or the deformation of some parts, resulting in the misalignment of generator and gearbox. When the misalignment occurs, it is easy to cause damage to gearbox gears, bearings and generator bearings and so on. Therefore, the study and fault isolation of the transmission system in the wind turbine is the key to ensuring its long stable operation. The misalignment belongs to a latent fault. When the fault is accumulated to a certain extent, the equipment is damaged seriously, leading to the outage of the wind turbine, affecting the power generation seriously, resulting in huge economic losses. For many years, how to isolate the latent fault accurately has been a difficult problem. This is mainly due to the fault feature of latent faults not being obvious, and researchers lack the awareness of the developing rules for latent fault. In order to identify and isolate the existence of the misalignment fault accurately and to overcome the difficulty of obtaining a large number of misalignment fault samples in reality, in this paper, Solidworks and Adams models are established according to the 1.5 MW wind turbine. The simulation of the transmission system is carried out in Adams under the normal operation, the parallel misalignment, the angle misalignment and the comprehensive misalignment. The vibration signal (angular acceleration) of a high speed shaft is extracted as the information (the established model and its effectiveness are shown in [7]). The methods of extracting fault features from vibration signals are mainly based on time-domain analysis, frequency-domain analysis and time-frequency domain analysis:  
1. The time-domain analysis is one of the most simple and direct analysis methods, and it is effective in isolating the fault in a certain extent. Except for the poor anti-interference of serious faults, the numerical values in time-domain are close to that in normal state, so it is easy to make misjudgments only using time-domain analysis. 2. The frequency-domain analysis of signal is the most commonly used method of mechanical equipment fault analysis. It can be used to get more intuitive fault information than by the time-domain analysis. However, the theoretical basis of frequency-domain analysis is the method of Fourier analysis, so it has sidedness, and cannot extract the features of vibration signal comprehensively. 3. The vibration signal is expressed in the time-domain and frequency-domain at the same time for the time-frequency domain analysis, and it has very prominent advantages. The main methods of feature extraction based on time-frequency analysis are as follows: short time Fourier Transform, Wigner-Ville Distribution, Wavelet Transform, Blind Source Separation, Empirical Mode Decomposition (EMD) and so on.