###### Abstract
Once the signal has been sampled and before obtaining the PSD, the high frequency noise is eliminated by means of a forward-backward filter. The variable used to predict the state of the multiplier and its RUL is the SNR calculated according to Equation (5). The SNR values are the input of a five-layer ANFIS model, with an inference system formed by a set of 16 Fuzzy rules of the IF-THEM form based on a first order Sugeno model and optimizing the parameters using an ANN. \[ \begin{array}{c}i_{sa}=i_{\xi T0}\cos(2\pi ft)+\sum\limits_{i}\frac{1}{2}A_{ sT_{i}}[\cos(2\pi(f-f_{i})t-\varphi_{Ti})+\cos(2\pi(f+f_{i})t+\varphi_{Ti})]\\ +\sum\limits_{j}\frac{1}{2}A_{sT_{j}}[\cos(2\pi(f-f_{j})t-\varphi_{Tj})+\cos (2\pi(f+f_{j})t+\varphi_{Tj})]\end{array}  \tag{4}\]  
\[NSR=\frac{P_{noise}}{P_{signal}}=\frac{P_{total}-P_{signal}}{P_{signal}} \tag{5}\]  
According to [31], although the rotor imbalance is manifested in the amplitude of the lateral harmonics of the fundamental frequency, the FFT and STFT fail to detect faults when the SNR is low, and the speed is not constant. As a single ANN would not be sufficient to cover the entire range of the electric generator speed, the authors propose dividing the interval in which the speed varies by several ranges and using one ANN for each range. The variables used correspond to wind speed, wind direction, pitch angle, turning speed and power output for one year, all of them obtained from the SCADA. To test the proposal, [31] simulates in Simulink a WT whose current signal is sampled at 5 kHz for 300 s. Applying FFT to 2 s signal segments, the spectrum formed by 250 components that become the input of the ANN is obtained. Having trained the model first with the signal in a healthy state and then with the signal containing the fault, the author states that it is possible to detect the frequency components associated with rotor eccentricity, which according to classical spectral analysis are given by Equation (6). \[f_{ecc}=[1\pm(2k-1/p)]f \tag{6}\]  
In [32], through the equations that relate voltage, current, flux, mutual inductance and torque between stator and rotor, a WT with DFIG is simulated in Matlab, but unlike [31], the faults studied are the one-phase fault and the inter-turns short circuit of the stator, while the model used is Fuzzy logic. The current signals from the three phases of the stator feed the Fuzzy system, which interprets them as linguistic variables (zero, small, medium,and large). From the database obtained with the measurements, the membership functions and 14 Fuzzy rules are built. In another simulation proposed in [33], the current signal from a DFIG and ANN are used to monitor islanding events of WTs that are part of a wind farm connected to the grid. Other models used include the detection of broken bars and inter-turns short circuits, using equations from Table 1[34]. In relation to the publications on the monitoring, detection, and diagnosis of failures in induction motors, using current signal and AI models, there is less research that deals with WTs. Some proposals are shown in Table 2. The components that have received the most attention are: bearings, gearbox, blades and electric generator. It can also be observed that among the models used, the following predominate: SVM, ANN, fuzzy logic and ANFIS. However, at the time of writing this research, only the works of [15; 35; 36; 37] deal with the use of the current signal based on data from WTs in operation, although none of them apply AI models. By way of summary, it can also be stated that:  
* Generally, signal processing and analysis techniques are combined with AI;
* Not just one AI model is used, but a combination of them;
* The component of the WTs that has received the most attention is the gearbox;
* The types of failures most analyzed are broken gears and broken teeth;
* In most cases, the current signal is from a DFIG; and
* Stator, rotor or both currents signal can be used.