A method of monitoring wind turbine condition, comprising the following steps: historical data acquiring step: acquiring historical SCADA data, and wind turbine reports corresponding to the historical SCADA data, wherein the historical SCADA data covers operation data of the wind turbine, and wherein the wind turbine reports covers: health condition of wind turbine diagnosed as normal or defective status, and defective component with corresponding fault details if health condition of wind turbine is diagnosed as defective status; model training step: training an overall model for overall diagnosing the wind turbine, and training different individual models for analyzing different components of the wind turbine based on the historical SCADA data and the corresponding wind turbine report, by establishing relationship between the historical SCADA data and the wind turbine report; overall diagnosing step: acquiring real time SCADA data, inputting the real time SCADA data to the trained overall model, obtaining the health condition of the wind turbine from the trained overall model, wherein the health condition of the wind turbine identifies whether the wind turbine is normal or defective status, and if defective status, which component is defective, and performing individual diagnosing step only if the trained overall model determines wind turbine as defective status; and individual diagnosing step: inputting the real time SCADA data to the trained individual model corresponding to the defective component, and obtaining the fault details of the defective component from the trained individual model corresponding to the defective component, wherein the fault details of the defective component indicate a specific fault reason of the defective component. historical data acquiring step: acquiring historical SCADA data, and wind turbine reports corresponding to the historical SCADA data, wherein the historical SCADA data covers operation data of the wind turbine, and wherein the wind turbine reports covers: health condition of wind turbine diagnosed as normal or defective status, and defective component with corresponding fault details if health condition of wind turbine is diagnosed as defective status; model training step: training an overall model for overall diagnosing the wind turbine, and training different individual models for analyzing different components of the wind turbine based on the historical SCADA data and the corresponding wind turbine report, by establishing relationship between the historical SCADA data and the wind turbine report; overall diagnosing step: acquiring real time SCADA data, inputting the real time SCADA data to the trained overall model, obtaining the health condition of the wind turbine from the trained overall model, wherein the health condition of the wind turbine identifies whether the wind turbine is normal or defective status, and if defective status, which component is defective, and performing individual diagnosing step only if the trained overall model determines wind turbine as defective status; and individual diagnosing step: inputting the real time SCADA data to the trained individual model corresponding to the defective component, and obtaining the fault details of the defective component from the trained individual model corresponding to the defective component, wherein the fault details of the defective component indicate a specific fault reason of the defective component.