A method for computer-implemented monitoring of a wind turbine , the wind turbine comprising a tower , a nacelle , a hub and a plurality of blades , where for each blade an activity signal (f A , f B , f C ) of the blade is detected at subsequent time points, where for each time point of at least some of the subsequent time points, the following steps are performed: a) predicting an activity signal (f AP , f BP , f CP ) of each blade at the respective time point by a separate data-driven model (DM1, DM2, DM3), where the predicted activity signal (f AP , f BP , f CP ) is an output value of the respective data-driven model (DM1, DM2, DM3) and where one or more activity signals (f A , f B , f C ) of blades other than the blade whose activity signal (f AP , f BP , f CP ) is the output value are input values of the respective data-driven model and where the respective data-driven model (DM1, DM2, DM3) has been learned by machine learning using training data comprising known input and output activity signals (f A , f B , f C ) occurred in the past during an undamaged state of the wind turbine ; b) determining for each data-driven model (DM1, DM2, DM3) a residual (re A , re B , re C ) between the predicted activity signal (f AP , f BP , f CP ) and the detected activity signal (f A , f B , f C ); c) checking a threshold criterion for one or more variables (va 1 , va 2 , va 3 ), where the values of the one or more variables (va 1 , va 2 , va 3 ) depend on the residuals (re A , re B , re C ) for all data-driven models (re A , re B , re c ), where a threshold (TH1, TH2, TH3) is defined for each variable (va 1 , va 2 , va 3 ) and the threshold criterion is fulfilled if the value of any variable (va 1 , va 2 , va 3 ) exceeds the threshold (TH1, TH2, TH3) defined for this variable (va 1 , va 2 , va 3 ); d) determining an abnormal operation state (AS) of the turbine , if the threshold criterion is fulfilled. a) predicting an activity signal (f AP , f BP , f CP ) of each blade at the respective time point by a separate data-driven model (DM1, DM2, DM3), where the predicted activity signal (f AP , f BP , f CP ) is an output value of the respective data-driven model (DM1, DM2, DM3) and where one or more activity signals (f A , f B , f C ) of blades other than the blade whose activity signal (f AP , f BP , f CP ) is the output value are input values of the respective data-driven model and where the respective data-driven model (DM1, DM2, DM3) has been learned by machine learning using training data comprising known input and output activity signals (f A , f B , f C ) occurred in the past during an undamaged state of the wind turbine ; b) determining for each data-driven model (DM1, DM2, DM3) a residual (re A , re B , re C ) between the predicted activity signal (f AP , f BP , f CP ) and the detected activity signal (f A , f B , f C ); c) checking a threshold criterion for one or more variables (va 1 , va 2 , va 3 ), where the values of the one or more variables (va 1 , va 2 , va 3 ) depend on the residuals (re A , re B , re C ) for all data-driven models (re A , re B , re c ), where a threshold (TH1, TH2, TH3) is defined for each variable (va 1 , va 2 , va 3 ) and the threshold criterion is fulfilled if the value of any variable (va 1 , va 2 , va 3 ) exceeds the threshold (TH1, TH2, TH3) defined for this variable (va 1 , va 2 , va 3 ); d) determining an abnormal operation state (AS) of the turbine , if the threshold criterion is fulfilled. <EOS>