This research aims to improve wind turbine maintenance by detecting faults early using only data from normal operation. The method analyzes vibration signals in both time and frequency domains and uses a variational autoencoder, a type of deep learning model, to learn normal behavior. It then detects anomalies by measuring how much new data deviate from this learned model. Tests on real turbine data show early and accurate detection of faults such as pitch issues and icing.