Condition monitoring of gear-based mechanical systems undergoing non-stationary operation conditions is in general very challenging. In particular, this issue is remarkable as regards wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of gearbox damages is proposed: the main idea is that vibrations are measured at the tower, instead that at the gearbox. This implies that measurements can be performed without impacting on the wind turbine operation, as desirable by the point of view of wind turbine practitioners. A test case study is discussed: it deals with a wind farm owned by Renvico, featuring 6 wind turbines with 2 MW of rated power each. The vibration measurements at a wind turbine suspected to be damaged and at reference wind turbines are processed through a multivariate Novelty Detection algorithm in the feature space. The application of this algorithm is justified by univari-ate statistical tests on the time-domain features selected and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine.
Condition monitoring of wind turbine gearboxes through on-site measurement and vibration analysis techniques
Astolfi D.;
2020-01-01
Abstract
Condition monitoring of gear-based mechanical systems undergoing non-stationary operation conditions is in general very challenging. In particular, this issue is remarkable as regards wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of gearbox damages is proposed: the main idea is that vibrations are measured at the tower, instead that at the gearbox. This implies that measurements can be performed without impacting on the wind turbine operation, as desirable by the point of view of wind turbine practitioners. A test case study is discussed: it deals with a wind farm owned by Renvico, featuring 6 wind turbines with 2 MW of rated power each. The vibration measurements at a wind turbine suspected to be damaged and at reference wind turbines are processed through a multivariate Novelty Detection algorithm in the feature space. The application of this algorithm is justified by univari-ate statistical tests on the time-domain features selected and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
