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

TL;DR

This paper proposes a digital twin framework that uses real-time SCADA data to predict the time to failure of wind turbine gearboxes, aiming to improve maintenance and reduce operational costs.

Contribution

It introduces a novel digital twin concept specifically designed for forecasting wind turbine gearbox failures using multivariate time-series data.

Findings

Continuous real-time data integration enhances failure prediction accuracy.

The framework provides actionable insights for proactive maintenance.

Potential to reduce downtime and operational costs.

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.