Main Bearing Fault Prognosis in Wind Turbines based on Gated Recurrent Unit Neural Networks

Abstract

The transition from onshore to offshore wind farms is an imminent fact in the future. It supposes to face hard challenges like difficulties to carry out offshore maintenance operations due to increased downtime (because of several causes like continuously bad environmental conditions) on wind farms. That is why, there is a need to improve maintenance and monitoring practices like those involved in condition-based area. This work proposes a methodology based on three key points: (i) a semi-supervised model built from a gated recurrent unit (GRU) neural network and by using only healthy real SCADA data, (ii) propose a fault prognosis indicator (FPI) to trigger warnings or fault alarms as such, and (iii) detect the main bearing fault several months in advance on a faulty wind turbine. The reported results show the excellent performance of the GRU trained model to predict the main bearing temperature as output by exploiting the capabilities of GRUs (recurrent-based neural networks) to decide what information to forget or preserve through time. In the FPI construction, the use of exponentially weighted moving average (EWMA) helps at the results to avoid the presence of false alarms that is very useful in any detection strategy. Finally, the stated methodology lets to detect the main bearing fault on a WT two months in advance at least, which contributes to plan maintenance actions ahead of time. Furthermore, in this way, the lifespan of this large component may be extended and wind turbine’s uptime may increase in a significant percentage.