## 5 Conclusions  
The signal processing techniques have represented an important advance regarding the detection and diagnosis of faults; however, in many cases they are not enough and must be combined with other mathematical models that generally assume some idealizations. Another alternative is artificial intelligence (AI) models, which from a conceptual point of view are characterized by their ability to adapt to uncertainty and to work with incomplete data. These AI models are used individually or together with signal processing techniques; however, when only the current signal of a WT in operation is available, but the signal has not been previously sampled in a healthy state or with some type of fault, the detection and diagnosis is complicated. It must also be noted that when failures are incipient and there are few failure records, the efficiency of AI models will be reduced.  
In the usual procedures, the diagnosis algorithms are trained using data from tests with healthy equipment and with equipment in which failures have been caused. However, these situations may not be extrapolated to real situations, in addition to decreasing the performance of the classification algorithms when working with unbalanced datasets.  
To improve the efficiency of AI models and wind turbine fault diagnosis procedures in cases of unbalanced data, this research proposes generating synthetic data using GANs. The  
Figure 8: Receiver Operating Characteristic curve of the K-means model trained with data generated by GANs.  
Figure 9: Confusion matrix of the ANN trained with synthetic data.  
methodology has shown its effectiveness for the early detection of failures due to broken bars in SCIG, in addition to allowing for improving the metrics of the AI model used.  
Although this study has focused on broken bars, the proposed model could be applied to detect other faults. At the output of the proposed model, another model based on ANN or Fuzzy logic could be added to obtain a more precise diagnosis of the failure studied (half section broken bar, one broken bar, two broken bars, many broken bars). It would also be advisable to continue with the research trying to build GANs, not only with MLP, but also with other AI models, and to use GANs not to generate a synthetic dataset, but to carry out the diagnosis exclusively through GANs. In this study, tabular data have been used; however, the proposed methodology could be tested with images. In addition, other types of signals could also be used, such as: vibration, acoustic and thermal.  
Conceptualization, Y.M.Z. and L.H.-C. methodology, Y.M.Z.; validation, Y.M.Z., L.H.-C. and O.D.-P.; formal analysis, Y.M.Z., L.H.-C. and O.D.-P.; resources, Y.M.Z. and L.H.-C.; writing--original draft preparation, Y.M.Z.; writing--review and editing, L.H.-C. and O.D.-P.; visualization, V.A.-G.; supervision, L.H.-C.; project administration, O.D.-P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by University of Guayaquil and CETASA.  
Not applicable.  
The authors would like to thank the University of Valladolid and University of Guayaquil for the assistance in the preparation of this research. We would also like to thank the company CETASA for allowing the acquisition of the signals and providing the necessary equipment. Thanks also to the anonymous reviewer for their assistance in the improvement of the study.  
The authors