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 (5A, 5B, 5C), where for each blade (5A, 5B, 5C) an activity signal (f A, f B, f C) of the blade (5A, 5B, 5C) 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 (5A, 5B, 5C) 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 (5A, 5B, 5C) other than the blade (5A, 5B, 5C) whose activity signal (f AP, f BP, f CP) is the output value are input values of the respective data-driven model (5A, 5B, 5C) 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 of the turbine, if the threshold criterion is fulfilled. a) predicting an activity signal (f AP, f BP, f CP) of each blade (5A, 5B, 5C) 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 (5A, 5B, 5C) other than the blade (5A, 5B, 5C) whose activity signal (f AP, f BP, f CP) is the output value are input values of the respective data-driven model (5A, 5B, 5C) 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 of the turbine, if the threshold criterion is fulfilled.