Remaining useful life and lifetime extension of wind turbine drivetrains

为了优化风电机组的维护决策，必须对风电机组传动系统的未来健康状态进行预测。预测是利用系统或组件过去和现在的状态监测数据来估计其未来的健康状态的过程。就停机时间和更换成本而言，风力涡轮机传动系统是一个关键的组件；因此，监测它并进行准确的预测是非常重要的。监测通常使用振动、SCADA和石油数据。融合上述多个数据源的集成决策支持系统可以在基于状态的监测方案下提高维护行动的可信度。该EngD将重点关注风力涡轮机传动系统故障检测，隔离和剩余使用寿命估计，使用先进的信号处理和机器学习方法，并考虑组件依赖性。工作内容包括：*研究适用于风力机振动信号的各种信号处理方法。*从多个数据源/数据流中提取健康指标。*研究各种适用的机器学习方法，利用提取的指标作为特征进行预测。*动力传动系统组件之间的模型依赖关系。*开发多组分降解模型。*模拟生命周期延长方案。这项工作将使用运行中的风力发电场的数据进行验证。作为一个合作研究项目，研究生将与自然电力公司和斯特拉斯克莱德大学的研究团队一起工作，在这两个组织中花费时间。

To optimally make decisions for wind turbine maintenance, predictions on the future health states of the wind turbine drivetrain must be carried out. Prognostics is the process whereby past and present condition monitoring data of a system or component is used to estimate its health state into the future. The wind turbine drivetrain is a critical subassembly in terms of downtime and replacement costs; therefore, it is very important to monitor it and perform accurate prognostics. Monitoring is usually done using vibration, SCADA, and oil data. An integrated decision support system fusing the aforementioned multiple sources of data can increase the confidence of a maintenance action under a condition-based monitoring scheme.This EngD will focus on the wind turbine drivetrain fault detection, isolation and remaining useful life esti-mation using advanced signal processing and machine learning methods and considering component de-pendencies. The work will involve the following:*Research of various signal processing methods applicable to wind turbine vibration signals.*Extraction of health indicators from multiple data sources/streams.*Research of various applicable machine learning methods for prediction using the extracted indica-tors as features.*Model dependencies between drivetrain components.*Develop a multi-component degradation model.*Model lifetime extension scenarios.The work will be validated using data from operating wind farms.As a collaborative research project, the research student will work together with Natural Power and the University of Strathclyde research teams, spending time in both organisations.