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Conference papers

AStrion assets for the detection of a main bearing failure in an onshore wind turbine

Xavier Laval 1 Guanghan Song 2 Zhong-Yang Li 2 Pascal Bellemain 1 Maxime Lefray 2 Nadine Martin 2 Alexis Lebranchu 2 Corinne Mailhes 3, 4
1 GIPSA-Services - GIPSA-Services
GIPSA-lab - Grenoble Images Parole Signal Automatique
2 GIPSA-SAIGA - GIPSA - Signal et Automatique pour la surveillance, le diagnostic et la biomécanique
GIPSA-DA - Département Automatique, GIPSA-DIS - Département Images et Signal
3 IRIT-SC - Signal et Communications
IRIT - Institut de recherche en informatique de Toulouse
Abstract : Monitoring the drive train of a wind turbine is still a challenge for reducing operationand maintenance costs and therefore decreasing cost of energy. In this paper, astandalone, data-driven and automatic tracking analyzer, entitled AStrion and alreadypresented in this conference, is applied on vibration data acquired during one full yearon a set of sensors located in the nacelle of two wind turbines in a wind farm in thePyrénées (France). These experimentations were realized thanks to KAStrion projectfunded by KIC InnoEnergy program.In the context of a particular case study, the main bearing failure of one of the two windturbines, this paper will highlight three main assets of AStrion strategy. A first asset willbe the application of the data validation module. According to the value of anonstationary index, the data measured on the sensor located on the main bearing closeto the failure have been discarded. This was justified afterwards by a dysfunction of thesensor. Then from the validated data acquired with a more remote sensor, a second assetwill be the trends of global features computed by AStrion which proved a strong linkwith maintenance operations on the mechanical components such as the greasing. Thethird asset will be the reading of other AStrion features associated to one specificcomponent. Indeed the trends of the features of the main bearing show evolutionsthroughout the year. A real time reading would have led to the conclusion of a severeevolution of the condition of this main bearing eight months before the failure and thestop of the machine. This study was carried out thanks to a narrow collaboration withthe operator of the wind farm.
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Submitted on : Monday, March 19, 2018 - 9:49:55 AM
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  • HAL Id : hal-01399027, version 2


Xavier Laval, Guanghan Song, Zhong-Yang Li, Pascal Bellemain, Maxime Lefray, et al.. AStrion assets for the detection of a main bearing failure in an onshore wind turbine. CM 2016 - MFPT 2016 - 13th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, Oct 2016, Paris, France. ⟨hal-01399027v2⟩



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