The document discusses using an artificial neural network approach for early fault detection of gearbox bearings in wind turbines. It introduces a framework for predictive maintenance management of wind turbines using condition monitoring. The approach applies an ANN to real data from gearbox bearings in 2 MW onshore wind turbines in Sweden. The results showed the ANN approach could provide early indications of severe damage in the monitored components.

1. AN ARTIFICIAL NEURAL NETWORK APPROACH FOR EARLY
FAULT DETECTION OF GEARBOX BEARINGS
ABSTRACT
Gearbox has proven to be a major contributor toward downtime in wind turbines. The
majority of failures in the gearbox originate from the gearbox bearings. An early indication of
possible wear and tear in the gearbox bearings may be used for effective predictive maintenance,
thereby reducing the overall cost of maintenance. This paper introduces a selfevolving
maintenance scheduler framework for maintenance management of wind turbines. Furthermore,
an artificial neural network (ANN)-based condition monitoring approach using data from
supervisory control and data acquisition system is proposed. The ANN-based condition
monitoring approach is applied to gearbox bearings with real data from onshore wind turbines,
rated 2 MW, and located in the south of Sweden. The results demonstrate that the proposed
ANN-based condition monitoring approach is capable of indicating severe damage in the
components being monitored in advance.