<p>Inverse Force Identification (IFI) methods are useful tools to estimate unknown forces on a structure or machine element. In wind turbine drivetrains IFI methods could be applied to identify forces at failure-critical and inaccessible locations and improve the accuracy of fault prognosis. This paper conducts a comparative study of several IFI methods (Parity Space Approach, Tikhonov regularization, Kalman filter) with the following criteria: Expected force estimation error under normal operating conditions and sensitivity to drivetrain model uncertainties. A 5 MW reference drivetrain consisting of two planetary and one parallel gear stage is modelled using multi-body simulation software. For a representative load case at nominal wind speed the internal drivetrain dynamics are computed. The IFI methods are then applied on simulated vibration measurements to identify local forces. The expected estimation error is quantified by comparison of identified and simulated forces using correlation measures in the time and frequency domain. A sensitivity analysis is conducted to assess the influence of model uncertainties such as stiffness and damping parameters. Lastly, the feasibility of implementing IFI methods in a commercial setting is discussed considering the expected estimation error and the requirements to the physical drivetrain model and to sensor placements.</p><p>Keywords: Inverse force identification, inverse problem, wind turbine drivetrain, fault prognosis, remaining useful life</p>

Estimation of Wind Turbine Gearbox Loads for Online Fatigue_x000B_Monitoring Using Inverse Methods

Paper Proceedings: https://asmedigitalcollection.asme.org/OMAE/OMAE2021/volume/85116

OMAE 2021

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