A METHOD FOR COMPUTER-IMPLEMENTED MONITORING OF A COMPONENT OF A WIND TURBINE

The invention refers to a method for computer-implemented monitoring of a component of a wind turbine, where the wind turbine is a first wind turbine (1) and the component is a first component (6, 7, 8) and where the method has access to a trained machine learning model (ML) which has been trained for one or more second components (6', 7', 8') of the same type of one or more second wind turbines (1'). The trained machine learning model (ML) is configured to provide an output (OU) referring to a predetermined fault (FT) occurring at a second component (6', 7', 8') of a second wind turbine (1') by processing vibration signals (VS') in a predetermined domain which are measured in the vicinity of the second component (6', 7', 8') during the operation of the second wind turbine (1'). In a step i), vibration signals (VS) in the predetermined domain measured in the vicinity of the first component (6, 7, 8) during the operation of the first wind turbine (1) are provided. In a step ii), the vibration signals (VS) are mapped to corresponding vibration signals (VS') valid for the second component (6', 7', 8') based on one or more given kinematic parameters (KP1) of the first component (6, 7, 8) and one or more given kinematic parameters (KP2) of the second component (6', 7', 8'). In a step iii), the machine learning model (ML) is applied to the vibration signals (VS') valid for the second component (6', 7', 8'), resulting in an output (OU) referring to the predetermined fault (FT) occurring at the first component (6, 7, 8).