Digital Twin Framework for Time to Failure Forecasting of Wind Turbine Gearbox: A Concept

• URL: http://arxiv.org/abs/2205.03513v1
• Date: Thu, 28 Apr 2022 05:51:48 GMT
• Title: Digital Twin Framework for Time to Failure Forecasting of Wind Turbine Gearbox: A Concept
• Authors: Mili Wadhwani, Sakshi Deshmukh, Harsh S. Dhiman
• Abstract summary: Wind turbine is a complex machine with its rotating and non-rotating equipment being sensitive to faults. Fault detection in wind turbines is often supplemented with SCADA data available from wind farm operators. Time-series analysis and data representation has become a powerful tool to get a deeper understating of the dynamic processes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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