A method is described for the early identification of material fatigue in drive train components of a wind turbine installation. In particular, a signal, representing revolutions per minute of a wind turbine shaft, is obtained and modulated by the azimuth angle measurement of the turbine blade. This signal is band passed at twice the frequency of rotation and Fourier transformed to extract amplitude values. An alert response can then be triggered when it is determined that there has been a change in a characteristic of the amplitude values such as the amplitude values increasing beyond a multiple of a determined baseline amplitude value.

Systems and methods are disclosed herein for predictive maintenance using computational models. The systems and methods can receive data from a supervisory control and data acquisition (SCADA) system and condition monitoring systems (CMSs), generate, using anomaly detectors, anomaly scores, and generate, using an augmented data fusion model, a health state prediction of a component of a machine. An extract, transform, and load (ETL) module can extract and transform data from a variety of sources, including SCADA systems, CMSs, and other computing devices to create model input data. A models module can analyze such model input data to determine which computational models can run on the input data. A prediction module and confidence interval data can hone the predictions created by the models module. A feedback module and the ETL module can be used on diagnostic data, configuration data, and updated SCADA and CMS data to improve the computational models.

The invention relates to a method for monitoring yawing fault events of a yaw system of a wind turbine. The yaw system comprises one or more actuators for driving the yaw system and a holding system to resist yaw rotation. The yaw system is arranged to provide yaw rotation in response to a yaw control signal. According to the method, the monitored yaw angle is compared with the yaw control signal, and based on the comparison, a correlation between a monitored change in the yaw angle and the yaw control signal is determined. A yawing fault event is determined dependent on the determined correlation.

A detection unit acquires detection data of a wind turbine generator. A controller generates abnormality information indicating an abnormality or an abnormality sign in the wind turbine generator based on the detection data. The controller transmits the abnormality information to a user terminal.

The present invention concerns a method for determining the production availability of an offshore wind farm comprising at least one floating wind turbine, the method comprising: obtaining wind farm data, obtaining strategy data relative to operation and maintenance resources to carry out an action on the floating wind turbine(s), obtaining meteorological data relative to an offshore environment for the offshore wind farm over a given period of time, determining motion parameters as a function of the wind farm data and of the meteorological data, and determining the production availability of the offshore wind farm in the offshore environment over the given period of time on the basis of the wind farm data, of the strategy data, of the meteorological data, and of the determined motion parameters.