Wind energy has emerged as a crucial solution in the search for sustainable and environmentally friendly energy sources. As global energy demand grows rapidly and the need to reduce greenhouse gas emissions increases, wind turbines have become a key technology for the transition to a cleaner and more sustainable energy future. This paper focuses on the application of autoencoder neural networks for the accurate and efficient detection of anomalies in wind turbines. The main objective is to significantly improve monitoring and predictive maintenance of wind turbines and thus enhance wind energy availability. The dataset used includes records of wind turbines with both, healthy and faulty conditions. Healthy records serve as a baseline to illustrate the expected amplitude of the turbine vibrations under optimal operation conditions. In contrast, failure records arising from blade surface erosion, blade unbalanced, and blade twist are used to evaluate the failure detection model. The proposed algorithm demonstrates a precision of 95.42% for blade unbalanced failures, 94.63% for blade surface erosion, and 87.54% for blade twist faults.