# Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features
## 1 Introduction
Due to the complexity of the fault mechanism of the wind turbine transmission system, the state information is different with different feature indexes, the sensitivity and regularity of the running state, as well as the clustering and separability in the model space not being the same. Single features or single domain features are difficult to fully reflect the different conditions of wind turbines with different degrees and types of faults. Therefore, it is necessary to construct a mixed feature library with time-domain, frequency-domain and time-frequency domain, which becomes one of the development trends of fault feature extraction. Many scholars have applied one or two of the above domains to extract the fault isolation feature based on vibration signal. For example, Ref. [8] extracted root mean square (RMS), peak value and kurtosis coefficient of the vibration signal, and combined with the time-domain waveform to judge whether there was fault in equipment. In Ref. [9], the kurtosis and peak value of the vibration signal  
Figure 1: Simplified model of a doubly fed wind turbine. were selected as the time-domain indexes, and then the wavelet packet algorithm was used to extract the frequency band energy and the 2-norm as the time-frequency domain indexes, the PCA (Principal Component Analysis) was used to confirm the principal component, and the crack fault of the wind turbine gearbox was diagnosed effectively. The EMD method was used to judge the working states and fault types of the gear by calculating EMD energy entropy of different vibration signals as the input features of Support Vector Machine in [10], but only the time-frequency domain feature was considered. Ref. [11] used a testing system for a wind turbine vibration test to collect vibration signal, and the time-domain and frequency-domain analysis were carried out to get the fault features of the rotor misalignment through the analysis of wind turbine generator, but the time-frequency analysis was not considered. In Ref. [12], the wavelet analysis was used to denoise the vibration signals of the wind turbine gearbox under normal, wear fault and broken tooth fault. Five feature parameters were extracted into the LVQ (Learning Vector Quantification) neural network, and the results showed that the method can identify the fault quickly and accurately. In Ref. [13], the time-domain of the peak, RMS and kurtosis of the vibration signal were extracted as the time-domain features of the fault gearbox. Combining observation and analysis of the normal signal and fault signal frequency-domain feature, the possible location of the failure was determined. In Ref. [14], the vibration signals of three typical states of normal conditions, tooth wear and tooth breakage of the gearbox in the wind turbine are analyzed, and the margin index, kurtosis index, peak index, pulse index, power spectrum entropy in the frequency-domain were extracted, the time-domain and frequency-domain features were the inputs in the fault isolation. In Refs. [15] and [16], the mixed-domain feature set is constructed to completely characterize the property of each fault by combining Empirical Mode Decomposition (EMD) with the Autoregression (AR) model coefficients. A large amount of literature shows that there are few reports on the method of combining the time-domain, frequency-domain and time-frequency domain features together to construct a mixed-domain feature library when researching the fault of the wind turbine transmission system. In order to extract the feature parameters which reflect the vibration signal as much as possible, and to make the fault isolation more reliable and accurate, in this paper, firstly, the time-domain, frequency-domain and time-frequency domain of the wind turbine vibration signal are combined to construct the mixed-domain feature library to obtain more comprehensive and accurate fault isolation information. The information entropy of the intrinsic mode function (IMF) component decomposed by EMD is used as the time-frequency feature.