Elasha Faris, Shanbr Suliman, Li Xiaochuan, Mba David
Faculty of Engineering, Environment & Computing, Coventry University, Coventry CV1 5FB, UK.
School of Water, Energy and Environment, Cranfield University, Bedfordshire MK43 0AL, UK.
Sensors (Basel). 2019 Jul 12;19(14):3092. doi: 10.3390/s19143092.
Deployment of large-scale wind turbines requires sophisticated operation and maintenance strategies to ensure the devices are safe, profitable and cost-effective. Prognostics aims to predict the remaining useful life (RUL) of physical systems based on condition measurements. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes to combine two supervised machine learning techniques, namely, regression model and multilayer artificial neural network model, to predict the RUL of an operational wind turbine gearbox using vibration measurements. Root Mean Square (RMS), Kurtosis (KU) and Energy Index (EI) were analysed to define the bearing failure stages. The proposed methodology was evaluated through a case study involving vibration measurements of a high-speed shaft bearing used in a wind turbine gearbox.
大规模风力涡轮机的部署需要复杂的运行和维护策略，以确保设备安全、盈利且具有成本效益。预测旨在基于状态测量来预测物理系统的剩余使用寿命（RUL）。分析状态监测数据、实施诊断技术并使用机械预测算法将实现对剩余寿命以及可能发生的故障的准确估计。本文提出结合两种监督式机器学习技术，即回归模型和多层人工神经网络模型，利用振动测量来预测运行中的风力涡轮机齿轮箱的剩余使用寿命。分析了均方根（RMS）、峰度（KU）和能量指数（EI）来定义轴承故障阶段。通过一个案例研究对所提出的方法进行了评估，该案例研究涉及对风力涡轮机齿轮箱中使用的高速轴轴承的振动测量。
