Abstract
Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for 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 drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features 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. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring.
Highlights
IntroductionThe diagnosis of gears and bearings faults of gearbox systems [1] is challenging, especially if the machine of interest is subjected to non-stationary operation conditions.Most of the wind turbines currently operating for industrial uses have a three-stage gearbox for transforming the rotation of the blades (order of 10 revolutions per minute) into the generator rotational speed
The diagnosis of gears and bearings faults of gearbox systems [1] is challenging, especially if the machine of interest is subjected to non-stationary operation conditions.Most of the wind turbines currently operating for industrial uses have a three-stage gearbox for transforming the rotation of the blades into the generator rotational speed
It is valuable to develop vibration measurement and analysis techniques that can reliably spread in the wind energy industry practice, and the present study aims at providing a contribution to this objective
Summary
The diagnosis of gears and bearings faults of gearbox systems [1] is challenging, especially if the machine of interest is subjected to non-stationary operation conditions.Most of the wind turbines currently operating for industrial uses have a three-stage gearbox for transforming the rotation of the blades (order of 10 revolutions per minute) into the generator rotational speed. Oil particle counting [6] and operation data analysis (especially temperatures, as, for example, in [7,8,9,10,11]) are widely used techniques for condition monitoring: they are more interpretable, but the drawback is that they furnish a later stage and more uncertain fault diagnosis, with respect to sub-component vibration spectra analysis This remark is supported quantitatively in the work [12], where large amounts of labeled wind turbine supervisory control and data acquisition (SCADA) and vibration data have been processed, and the conclusion is that operation data can be used for reliably diagnosing a failure approximately one month before it occurs, while high frequency vibration data can be used to extend the accurate prediction capability to five to six months before failure. Using operation data analysis, the fault diagnosis can successfully be performed through Artificial Intelligence techniques approximately 75% of the time, while, using vibration data, this percentage rises to 100%
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