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T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case

Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements †

Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Surveillance, Vibrations, Shocks and Noise (SURVISHNO 2019), Lyon, France, 8–10 July 2019.
Energies 2020, 13(6), 1474;
Received: 30 January 2020 / Revised: 10 March 2020 / Accepted: 17 March 2020 / Published: 20 March 2020
(This article belongs to the Special Issue Lifetime Extension of Wind Turbines and Wind Farms)
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. View Full-Text
Keywords: wind energy; wind turbines; condition monitoring; vibration analysis; signal processing wind energy; wind turbines; condition monitoring; vibration analysis; signal processing
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MDPI and ACS Style

Castellani, F.; Garibaldi, L.; Daga, A.P.; Astolfi, D.; Natili, F. Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements. Energies 2020, 13, 1474.

AMA Style

Castellani F, Garibaldi L, Daga AP, Astolfi D, Natili F. Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements. Energies. 2020; 13(6):1474.

Chicago/Turabian Style

Castellani, Francesco, Luigi Garibaldi, Alessandro Paolo Daga, Davide Astolfi, and Francesco Natili. 2020. "Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements" Energies 13, no. 6: 1474.

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