Digital Twin Framework For Time To Failure Forecasting Of Wind Turbine
Gearbox: A Concept
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.