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Open AccessArticle

Monitoring the Oil of Wind-Turbine Gearboxes: Main Degradation Indicators and Detection Methods

1
OELCHECK GmbH, Kerschelweg 29, 83098 Brannenburg, Germany
2
Fraunhofer Institute for Wind Energy Systems IWES, Am Luneort 100, 27572 Bremerhaven, Germany
*
Author to whom correspondence should be addressed.
Machines 2018, 6(2), 25; https://doi.org/10.3390/machines6020025
Received: 15 March 2018 / Revised: 2 June 2018 / Accepted: 5 June 2018 / Published: 8 June 2018
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
Oil condition monitoring is a common practice in the wind industry. However, the published research about oil degradation in wind turbine gearboxes is limited. This paper aims at providing new information on the oil degradation process by analyzing wind turbine gearbox oils aged in the laboratory and in the field. Oil samples were analyzed in the laboratory and two sensors were used to determine the oil condition by means of dielectric constant and conductivity measurements. Additionally, micropitting tests were carried out for three oils with different base stocks. The results of this study show that viscosity changes of the oils from the field were not significant.Extreme pressure additives depletion and the increase of the iron content are among the most relevant degradation indicators. The oil sensors used in this study provided limited information on the oil degradation process. The accuracy of the sensors was affected by the oil type and its measurement range. The results of the micropitting tests showed that even aged oils exhibited a high micropitting resistance. View Full-Text
Keywords: wind turbines; oil condition monitoring; gearboxes; oil sensors; oil analysis; oil degradation; oil sampling wind turbines; oil condition monitoring; gearboxes; oil sensors; oil analysis; oil degradation; oil sampling
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MDPI and ACS Style

Coronado, D.; Wenske, J. Monitoring the Oil of Wind-Turbine Gearboxes: Main Degradation Indicators and Detection Methods. Machines 2018, 6, 25.

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