# Wind Turbine Gearbox Fault Diagnosis Based on Improved EEMD and Hilbert Square Demodulation
## 1 Introduction  
In order to harvest wind energy more efficiently, wind turbines are becoming larger and more complex. As a result, the fault rates of wind turbines are increasing, which impacts enormously on the cost of wind energy [1]. The gearbox is the major component of a wind turbine drive train and is costly and vulnerable to failure, inevitably causing the unit to stop working [2]. Accordingly, investigation into fault diagnosis for the wind turbine gearbox is becoming a popular field of research. Typically, gear tooth damages and bearing faults are the most frequent faults. Since vibration signals carry much information related to the system's dynamical characteristics, vibration analysis is a common approach for wind turbine gearbox fault diagnosis, especially with respect to the rotation parts [3; 4]. However, the wind turbine working environment is strict and poor, which makes the vibration signals non-linear and non-stationary. Although traditional time domain and frequency analysis techniques, such as energy analysis, kurtosis, crest factor and spectrum analysis, have been widely used in fault diagnosis, these methods have only been effective in a stationary signal process. When it comes to non-stationary signal analyzing, the diagnostic performance has usually been unsatisfactory. For this reason, many time-frequency analysis techniques, such as wavelet packet transform (WPT) [5; 6], empirical model decomposition (EMD) [7; 8; 9] and independent component analysis (ICA) [10], have been applied to deal with the non-linear and non-stationary characteristics exhibited in the vibration signals. [PERSON] et al. integrated EEMD and ICA for gearbox bearing fault diagnosis [11]. [PERSON] et al. (2000) proposed a method based on wavelet packet decomposition and Hilbert-Huang transform (WPD-HHT) and successfully applied it to spindle bearings condition monitoring [12]. [PERSON] et al. (2007) used ICA to detect signal transients caused by localized gear damage [13]. [PERSON] et al. (2013) developed an EEMD-based wind turbine bearing failure detecting method using the generator stator current homopolar component [14]. Although those techniques made some progress into fault diagnosis, they had their own disadvantages. WPT could not process signals self-adaptively and needed a massive amount of data to ensure accurate results. EMD, a self-adaptive signal processing method, could decompose the non-stationary signal into several intrinsic mode functions (IMFs), which were almost orthogonal. However, mode mixing was a huge shortcoming, which restricted the application of EMD in many engineering situations. ICA extracted the transient feature without the need for prior information; however, this algorithm required redundant information measured from multiple sensors. Advanced signal processing techniques needs to be developed for this challenging task. EEMD, an improvement of EMD, was presented by [PERSON] and [PERSON] (2009) to overcome mode mixing [15]. Not only did EEMD have the virtue of self-adaptability, but it also eliminated mode mixing by adding noise to the original signal. It had an absolute advantage in dealing with non-stationary and nonlinear signals. However, the performance of EEMD was also affected by parameters, such as the amplitude of added noise and the number of ensemble trials. Several efforts have been made to explore choosing proper values of these parameters to evaluate the performance of EEMD. [PERSON] et al. (2010) investigated the effect of the above-mentioned parameters pertinent to EEMD and improved it by replacing white noise with a finite bandwidth noise [16]. Both [PERSON] et al. (2005) and [PERSON] investigated the effect of sampling frequency on EMD and proposed a sampling limit [17; 18]. However, the factor of added noise made the effect of sampling frequency on EEMD different from EMD. In this paper, the effect of sampling frequency on EEMD is quantitatively analyzed, and a criterion is proposed on how to select a resampling frequency according to the analysis result.