WINDTWIN: Condition monitoring of wind turbine gearbox toward digital twin ecosystem
Lead Research Organisation:
Brunel University London
Department Name: Electronic and Electrical Engineering
Abstract
The wind turbine gearbox (WTG) unusually operates in harsh working environments, making the WTG prone to failures and resulting
in unexpected shutdowns and enormous economic loss. Therefore, it is critical to conduct condition monitoring and predictive
maintenance for the safe and efficient operation of wind turbines. This project aims to develop a digital twin ecosystem for the health
management of wind turbine gearboxes. More specifically, an intelligent modeling calibration algorithm is developed for the high-fidelity model establishment of WTG. Also, novel fatigue models are developed for simulating the degradation characteristics of WTG.
To achieve the seamless convergence of the physical structures of WTG with its virtual model, first, novel health indicators are
developed for estimating the WTG degradation progression; second, the Bayesian inference is improved to include uncertainties
existing in measurements and models for bridging the physical structures and virtual models. The novel health indicators and
improved Bayesian inference can help update the virtual model in real-manner, ensuring the degradation behaviors of WTG can be
well revealed and reflected by the virtual models. Moreover, novel transfer learning algorithms are developed to minimize the
discrepancy between the virtual models and individual wind turbines and extend the capability of the developed WTG digital twin
ecosystem. With the developed digital twin WTG health management ecosystem, safe and reliable operations of wind turbines can be
realized, and the maintenance cost and downtime of the wind turbine are expected to be reduced significantly (around 35% and 75%,
respectively); also, the productivity of wind turbines could increase by 30%. The unique research approach of this fellowship will be
hosted by Prof. Asoke Nandi from Brunel University London and co-supervised by secondment supervisor Prof. Daniele Dini from
Imperial College London.
in unexpected shutdowns and enormous economic loss. Therefore, it is critical to conduct condition monitoring and predictive
maintenance for the safe and efficient operation of wind turbines. This project aims to develop a digital twin ecosystem for the health
management of wind turbine gearboxes. More specifically, an intelligent modeling calibration algorithm is developed for the high-fidelity model establishment of WTG. Also, novel fatigue models are developed for simulating the degradation characteristics of WTG.
To achieve the seamless convergence of the physical structures of WTG with its virtual model, first, novel health indicators are
developed for estimating the WTG degradation progression; second, the Bayesian inference is improved to include uncertainties
existing in measurements and models for bridging the physical structures and virtual models. The novel health indicators and
improved Bayesian inference can help update the virtual model in real-manner, ensuring the degradation behaviors of WTG can be
well revealed and reflected by the virtual models. Moreover, novel transfer learning algorithms are developed to minimize the
discrepancy between the virtual models and individual wind turbines and extend the capability of the developed WTG digital twin
ecosystem. With the developed digital twin WTG health management ecosystem, safe and reliable operations of wind turbines can be
realized, and the maintenance cost and downtime of the wind turbine are expected to be reduced significantly (around 35% and 75%,
respectively); also, the productivity of wind turbines could increase by 30%. The unique research approach of this fellowship will be
hosted by Prof. Asoke Nandi from Brunel University London and co-supervised by secondment supervisor Prof. Daniele Dini from
Imperial College London.
