Development of a FULly InTellIgent Maintenance FrAmework for PrognosTic Health ManagemEnt of Floating Offshore Wind Turbines (ULTIMATE)

该项目旨在开发一种完全智能的解决方案，以应对浮式海上风力涡轮机(FOWT)预测健康管理的挑战。本研究将通过将智能和高精度的数据集相结合来解决大数据问题，开发一种具有不确定性量化的物理信息深度神经网络(PIDNN-UQ)，用于实时诊断和预测。为了研究疲劳行为和疲劳机理，将对多物理场耦合作用下的FOWT进行数值模拟。将开发用于FoWT准确建模和分析的智能(以数据为中心)疲劳机制数据库，以便于实时诊断和预测。本研究将设计并实现具有物理信息能力的多任务PIDNN-UQ模型，以提高模型的知识性和不确定性的量化。这将使该模型能够诊断、量化和预测FOWTs的剩余使用寿命。海风5 x 6兆瓦FOWT(浮动风电场)的实验和现场数据以及固定底部风力涡轮机(RAVE)的开源数据将用于验证和检查终极风电机组的性能。研究成果将有助于在预测性维护、实时了解FoWT的操作和性能方面取得进展。该项目还将有助于学习将机器学习应用于近海工程和可再生能源系统的知识。这将加强O&M、结构完整性、数据科学和应用数学的课程发展。工程，机械系统智能操作和维护的数学理论。该项目通过开发基于PHM方法的智能维护方法，以最少的人机接口提供最佳的FoWT操作，并提高了安全性和可靠性，从而使行业受益。

This project aims to develop a fully intelligent solutions to the challenges of prognostic health management of floating offshore wind turbines (FOWT). The research will develop a Physics Informed Deep Neural Network with Uncertainty Quantification (PIDNN-UQ) for real-time diagnosis and prognosis by combining smart and high precision dataset to address big data problem. Numerical simulations of FOWT in coupled multi-physical fields will be conducted to investigate fatigue behaviours and mechanisms. Smart (data-centric) databases of fatigue mechanisms for accurate modelling and analysis of FOWT will be devloped to facilitate realt-time diagnosis and prognosis. The study will design and implement multi-tasking PIDNN-UQ models with physics-informed capability to improved model's knowledge and uncertainty quantification. This will enable the model to diagnose, quantify and predict the remaining useful lifetime of FOWTs. Experimental and field data from Hywind 5 x 6MW FOWTs (Floating wind farm) and open source data from fixed bottom wind turbines (RAVE) would be used to validate and examine the performance of the ULTIMATE. Outcomes of the research will contribute to advances in predictive maintenance, understanding of operation and performance of FOWT in real time. This project will also contribute to knowledge in machine learning application to offshore engineering and renewable energy systems. This will enhance curriculum development in O&M, structural integrity, data science and applied mathematics. engineering, mathematical theories of intelligent operation and maintenance of mechanical systems. The project benefits the industry by developing intelligent maintenance methodologies based on PHM methods that delivers optimal FOWT operation with minimal human interface and improved safety and reliability.