An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples

Abstract

The wind power industry suffers from unexpectedly high failure rates in wind turbine high-speed shaft bearings (HSSBs). To reduce cost and improve availability, the industry needs an accurate fault prognostic and remaining useful life (RUL) capability. A reliable prognostic allows maintainers to better define when maintenance can be performed, improving availability or allowing for opportunistic maintenance. Wind turbines operate under harsh conditions and condition monitoring data shows the environment to be both non stationary behavior with high noise. This paper proposes a practical and effective data-driven methodology that can be applied for RUL prediction of HSSBs. A new health indicator (HI) is constructed based on the entropy measure of the so called “spectral shape factor” (SSF), after strengthening the original signal by Teager energy operator (TEO). An Elman neural network (ENN) is used for RUL estimation. Furthermore, prediction intervals of the RUL estimates are computed based on the trained ENN model in order to quantify the errors associated with the prediction. The methodology is validated using a real word data collected form 2 MW Suzlon S88 wind turbine.