This master thesis aims to demonstrate the effectiveness of a fault detection strategy in the structure of the wind turbine through a new strategy involving singular spectrum analysis (SSA), statistical methods and methods based on frequency. Based on a dynamic model of wind turbine, the time series behavior was obtained in the first instance without presenting any system failure and a second instance of system failure. From these series we can intervene with SSA and with statistical methods (variance, means, covariance, Fisher criteria) to design the fault detection system. The baseline will be designed with 850 healthy samples and a total sampling time of 6.25 seconds. This baseline which will provide us with the components to be compared so that the system can detect various faults that occur (fault types: fixed value, gain factor, offset and dynamics changed) in an efficient way. The results show that a sensor fault system with a high percentage of effectiveness was designed. The greater or lesser effectiveness thereof will depend on the established base line and the components used.