An insight into wind turbine planet bearing fault prediction using SCADA data

Koukoura, Sofia and Carroll, James and McDonald, Alasdair (2018) An insight into wind turbine planet bearing fault prediction using SCADA data. Proceedings of the European Conference of the PHM Society, 4 (1).

[img]
Preview
Text (Koukoura-etal-2018-An-insight-into-wind-turbine-planet-bearing)
Koukoura_etal_2018_An_insight_into_wind_turbine_planet_bearing.pdf
Final Published Version
License: Creative Commons Attribution 3.0 logo

Download (2MB)| Preview

    Abstract

    Condition based maintenance is being adopted into the decision making process of wind farms, in order to reduce operation costs. SCADA systems are integrated in wind turbines, providing low frequency operational data and are increasingly being used in condition monitoring. The aim of this paper is to explore how can wind turbine gearbox components be monitored using SCADA data. The proposed methodology utilises 10-minute averaged data. Data preprocessing is applied using a clustering filter in order to improve prediction confidence. Normal behaviour models are used to predict potential faults. The efficacy of the proposed methodology is demonstrated with a case study using SCADA data from three operating wind turbines that have a double planetary stage gearbox. Historic data is collected for more than a year before the occurrence of a bearing failure on a planet of the first planetary stage. The case study results indicate the potential importance of generator speed estimation for planet bearing faults. A successful prediction of the bearing health state can be performed through this model and some insight is given into into the optimal SCADA sensors utilization for this type of failure mode.