Fault diagnosis of wind turbine gearboxes through temperature and vibration data

Gearbox faults are one of the most common and severe causes of energy losses in large wind turbine technology. Further, degradation of gearboxes is an elusive phenomenon by the point of view of diagnostics. Yet, nowadays the widespread diffusion of Supervisory Control And Data Acquisition (SCADA) control systems is a keystone for fault prevention. It is desirable to conjugate accuracy of the outputs with intuitiveness and reasonable computational cost. The present work deals with these issues: some methods are proposed for data mining of SCADA gearbox temperature and vibration measurements. In particular, a model based on Artificial Neural Networks (ANN) is proposed and its performances are compared against similar approaches in the literature. It arises that vibration analysis at the time scale of SCADA data is not effective for fault diagnosis, even if powered by the artificial intelligence of the ANN, while the proposed ANN model for gearbox temperatures is useful for early fault diagnosis. The method is tested on the data sets of a wind farm in southern Italy and it is shown that it is useful for the diagnosis of incoming faults to three out of nine wind turbines of the site.