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

开发用于浮动式海上风力发电机预测健康管理的完全智能维护框架（终极版）

• 批准号: EP/Y014235/1
• 负责人: Musa Bashir
• 金额: $ 25.55万
• 依托单位: Liverpool John Moores University
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY014235%2F1
• 关键词: Development; FULly; InTellIgent; Maintenance; FrAmework

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.

该项目旨在开发一种完全智能的解决方案，以应对浮动海上风力涡轮机（FOWT）的预测健康管理挑战。该研究将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），通过结合智能和高精度数据集来解决大数据问题，从而实现实时诊断和预后。将进行耦合多物理场的FOWT的数值模拟，以研究疲劳行为和机制。将开发用于精确建模和分析FOWT的疲劳机制的智能（以数据为中心）数据库，以促进实时诊断和预后。本研究将设计并实作多任务PIDNN-UQ模型，并将其应用于多任务PIDNN-UQ模型中，以增进模型的知识与不确定性量化。这将使该模型能够诊断、量化和预测FOWT的剩余使用寿命。来自Hywind 5 x 6 MW FOWT（浮动风电场）的实验和现场数据以及来自固定底部风力涡轮机（RAVE）的开源数据将用于验证和检查ULTIMATE的性能。研究结果将有助于预测性维护的进步，理解操作和性能的FOWT在真实的时间。该项目还将有助于将机器学习应用于海上工程和可再生能源系统。这将加强O&M，结构完整性，数据科学和应用数学的课程开发。工程、数学理论的智能化操作与维护机械系统。该项目通过开发基于PHM方法的智能维护方法使行业受益，该方法以最少的人机界面提供最佳FOWT操作，并提高安全性和可靠性。