Digital twin‐driven online intelligent assessment of wind turbine gearbox

Abstract Remaining useful fatigue life monitoring of wind turbine drivetrains is important. However, the implementation of real‐time monitoring often faces efficiency and accuracy challenges. In order to resolve this, this paper proposes a vibration‐based damage monitoring digital twin (VBDM‐DT) that enables the online intelligent evaluation of wind turbine gearboxes, using gear tooth surface durability as an example fatigue mode. The VBDM‐DT integrates a random wind load model, a high‐fidelity dynamics model, and a fatigue damage model. The random wind load model takes the wind speed from the supervisory control and data acquisition (SCADA) as input to estimate the input torque of the drivetrain in real time. Simultaneously, VBDM‐DT uses the vibration signals from the condition monitoring system (CMS) to intelligently calibrate the dynamics model, allowing it to be continuously adjusted and optimized in response to actual vibrations. The fatigue damage model takes the real‐time dynamic loads estimated by the high‐fidelity dynamic model as input to achieve real‐time fatigue damage monitoring of key components in the wind turbine gearbox. Applying the VBDM‐DT model to a 2 MW wind turbine gearbox, the results indicate that the model provides satisfactory accuracy in estimating input loads and good adaptability in intelligent calibration of the dynamic model. Based on this model, the fatigue life estimation for gears and bearings is more credible and reliable.