WINDTWIN: Condition monitoring of wind turbine gearbox toward digital twin ecosystem

风力涡轮机齿轮箱（WTG）在恶劣的工作环境中异常工作，使得WTG容易发生故障，造成意外停机和巨大的经济损失。因此，进行状态监测和预测性维护对于风力发电机的安全高效运行至关重要。该项目旨在为风力涡轮机齿轮箱的健康管理开发一个数字孪生生态系统。更具体地说，智能建模校准算法开发的高保真模型建立的风力发电机。此外，新的疲劳模型的开发，用于模拟风力发电机的退化特性。为了实现风力发电机的物理结构与虚拟模型的无缝衔接，首先，开发了新的健康指标来估计风力发电机的退化进程;其次，改进了贝叶斯推理，以包括测量和模型中存在的不确定性，用于桥接物理结构和虚拟模型。新的健康指标和改进的贝叶斯推理可以帮助实时更新虚拟模型，确保WTG的退化行为可以很好地揭示和反映的虚拟模型。此外，开发了新的迁移学习算法，以最大限度地减少虚拟模型和单个风力涡轮机之间的差异，并扩展所开发的WTG数字孪生生态系统的能力。通过开发的数字孪生风力发电机组健康管理生态系统，可以实现风力发电机组的安全可靠运行，预计风力涡轮机的维护成本和停机时间将大幅降低（分别约为35%和75%），风力发电机组的生产率也将提高30%。该奖学金的独特研究方法将由伦敦布鲁内尔大学的Asoke南迪教授主持，并由帝国理工学院伦敦的借调导师Daniele Dini教授共同监督。

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.