Abstract: Provided is a method and arrangement of estimating replacement schedule of a, in particular mechanical, component of a wind turbine, the method comprising: estimating remaining producible energy until this component is to be replaced.

Abstract: Provided is a method for computer-implemented monitoring of a component of a wind turbine, having access to a trained machine learning model which has been trained for one or more components of the same type of wind turbines. The trained machine learning model is configured to provide an output referring to a predetermined fault occurring at a component of a wind turbine by processing vibration signals in a predetermined domain which are measured in the vicinity of the component during the operation of the wind turbine. Vibration signals are mapped to corresponding vibration signals valid for the component based on one or more given kinematic parameters of the component and one or more given kinematic parameters of another component. The machine learning model is applied to the vibration signals valid for the component, resulting in an output referring to the predetermined fault occurring at the another component.

Abstract: A method includes determining a sequence of first coefficient estimates of a communication channel based on a sequence of pilots arranged according to a known pilot pattern and based on a receive signal, wherein the receive signal is based on the sequence of pilots transmitted over the communication channel. The method further includes determining a sequence of second coefficient estimates of the communication channel based on a decomposition of the first coefficient estimates in a dictionary matrix and a sparse vector of the second coefficient estimates, the dictionary matrix including filter characteristics of at least one known transceiver filter arranged in the communication channel.

Abstract: A method includes determining a sequence of first coefficient estimates of a communication channel based on a sequence of pilots arranged according to a known pilot pattern and based on a receive signal, wherein the receive signal is based on the sequence of pilots transmitted over the communication channel. The method further includes determining a sequence of second coefficient estimates of the communication channel based on a decomposition of the first coefficient estimates in a dictionary matrix and a sparse vector of the second coefficient estimates, the dictionary matrix including filter characteristics of at least one known transceiver filter arranged in the communication channel.