Fault diagnosis of wind turbine gearboxes through on-site measurements and vibrational signal processing

Condition monitoring of wind turbine gearboxes has attracted an impressive amount of attention in the wind energy literature. This happens for practical issues, as gearbox damages account for at least the 20% of wind turbines operational unavailability, and for scientific issues as well, because the condition monitoring of gear-based mechanical systems undergoing non-stationary operation is particularly challenging. The present work is devoted to the diagnosis of gearbox damages through a novel approach, designed exclusively for this study, based on on-site measurements and data post-processing. The main point of this method is the relatively easy repeatability, also for wind turbine practitioners, and its low impact on wind turbine operation: actually, the measuring site is not the gearbox itself, but the tower, further from the gearbox but in an easily accessible place. A real test case has been considered: a multi mega-watt wind turbine sited in Italy and owned by the Renvico company. The vibration measurements at the wind turbine suspected to be damaged and at a reference wind turbine are processed through a multivariate Novelty Detection algorithm in the feature space. The application of this algorithm is justified by univariate statistical tests on the time-domain features selected and by a visual inspection of the dataset via Principal Component Analysis. Finally, the novelty indices based on such time-domain features, computed from the accelerometric signals acquired inside the turbine tower, prove to be suitable to highlight a damaged condition in the wind-turbine gearbox, which can be then successfully monitored.