论文标题
数字双胞胎框架以预测风力涡轮机变速箱的时间：一个概念
Digital Twin Framework for Time to Failure Forecasting of Wind Turbine Gearbox: A Concept
论文作者
论文摘要
风力涡轮机是一台复杂的机器，其旋转和非旋转设备对故障敏感。由于磨损的增加，风力涡轮机的维护方面至关重要。风力涡轮机组件的意外故障会导致O \＆M成本增加，这最终会降低风电场的有效捕获。风力涡轮机中的故障检测通常会以每次序列格式的形式以10分钟的样本间隔补充了来自风电场运营商的SCADA数据。此外，时间序列分析和数据表示已成为一种强大的工具，可以更深入地低估风力涡轮机中复杂机械中的动态过程。风力涡轮机SCADA数据通常以多元时间序列的形式获得，其变量，例如变速箱油温，变速箱轴承温度，Nacelle温度，转子速度和产生的主动功率。在此预印本中，我们讨论了数字双胞胎的概念，以便时间预测风力涡轮机变速箱的时间，在该变速箱中，预测模块不断使用实时SCADA数据进行更新，并为风电场运营商生成有意义的见解。
Wind turbine is a complex machine with its rotating and non-rotating equipment being sensitive to faults. Due to increased wear and tear, the maintenance aspect of a wind turbine is of critical importance. Unexpected failure of wind turbine components can lead to increased O\&M costs which ultimately reduces effective power capture of a wind farm. Fault detection in wind turbines is often supplemented with SCADA data available from wind farm operators in the form of time-series format with a 10-minute sample interval. Moreover, time-series analysis and data representation has become a powerful tool to get a deeper understating of the dynamic processes in complex machinery like wind turbine. Wind turbine SCADA data is usually available in form of a multivariate time-series with variables like gearbox oil temperature, gearbox bearing temperature, nacelle temperature, rotor speed and active power produced. In this preprint, we discuss the concept of a digital twin for time to failure forecasting of the wind turbine gearbox where a predictive module continuously gets updated with real-time SCADA data and generates meaningful insights for the wind farm operator.
