Digital twin modeling for predictive maintenance of gearboxes in floating offshore wind turbine drivetrains

This paper presents a multi-degree of freedom torsional model of drivetrain system as the digital twin model for monitoring the remaining useful lifetime of the drivetrain components. An algorithm is proposed for the model identification, which receives the torsional response and estimated values of rotor and generator torques, and calculates the drivetrain dynamic properties, e.g. eigenvalues, and torsional model parameters. The applications of this model in prediction of gearbox remaining useful lifetime is discussed. The proposed method is computationally fast, and can be implemented by integrating with the current turbine control and monitoring system without a need for a new system and sensors installation. A test case, using 5 MW reference drivetrain, has been demonstrated.

[1]  A. Nejad,et al.  On Digital Twin Condition Monitoring Approach for Drivetrains in Marine Applications , 2019, Volume 10: Ocean Renewable Energy.

[2]  Gabriel Synnaeve,et al.  Scaling Up Online Speech Recognition Using ConvNets , 2020, INTERSPEECH.

[3]  Torgeir Moan,et al.  Effect of Axial Acceleration on Drivetrain Responses in a Spar-Type Floating Wind Turbine , 2019, Journal of Offshore Mechanics and Arctic Engineering.

[4]  Darrell F. Socie,et al.  Simple rainflow counting algorithms , 1982 .

[5]  James F. Manwell,et al.  Book Review: Wind Energy Explained: Theory, Design and Application , 2006 .

[6]  Wenchao Wu,et al.  Research on Meshing Stiffness and Vibration Response of Pitting Fault Gears with Different Degrees , 2020 .

[7]  Carlo Gorla,et al.  Bending and contact fatigue strength of innovative steels for large gears , 2014 .

[8]  Sergio Martín-Martínez,et al.  Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review , 2020, Energies.

[9]  Ahmet Kahraman,et al.  Natural Modes of Planetary Gear Trains , 1994 .

[10]  Farid K. Moghadam,et al.  Evaluation of PMSG‐based drivetrain technologies for 10‐MW floating offshore wind turbines: Pros and cons in a life cycle perspective , 2020 .

[11]  Yaguo Lei,et al.  A probability distribution model of tooth pits for evaluating time-varying mesh stiffness of pitting gears , 2018, Mechanical Systems and Signal Processing.

[12]  J. Jonkman,et al.  Definition of a 5-MW Reference Wind Turbine for Offshore System Development , 2009 .

[13]  Stefan Faulstich,et al.  Performance and Reliability of Wind Turbines: A Review , 2017 .

[14]  G. Golub,et al.  Unsymmetric positive definite linear systems , 1979 .

[15]  Stephen K. Robinson,et al.  Development of an integrated simulation platform for real-time task performance assessment , 2015, 2015 IEEE Aerospace Conference.

[17]  Torgeir Moan,et al.  Development of a 5 MW reference gearbox for offshore wind turbines , 2016 .

[18]  Amir Rasekhi Nejad,et al.  Theoretical and experimental study of wind turbine drivetrain fault diagnosis by using torsional vibrations and modal estimation , 2021 .

[19]  Torgeir Moan,et al.  On long-term fatigue damage and reliability analysis of gears under wind loads in offshore wind turbine drivetrains , 2014 .

[20]  Ming J. Zuo,et al.  The influence of tooth pitting on the mesh stiffness of a pair of external spur gears , 2016 .

[22]  Wen Chen,et al.  Feasibility study on the least square method for fitting non-Gaussian noise data , 2017, 1705.01451.

[23]  Mert Pilanci,et al.  Structured Least Squares Problems and Robust Estimators , 2010, IEEE Transactions on Signal Processing.