This paper proposes a method for evaluating the lubrication performance of wind turbine main bearings under real working conditions using a SCADA (supervisory control and data acquisition) system. A dataset is established, and simulations are performed to replicate the real meteorological and operational conditions of the wind turbine. The load conditions and lubrication performance of the main bearings are then studied under these real conditions. The results demonstrate that the front main bearing works under mixed and even boundary lubrication regimes, while the back main bearing has a 62.2% probability of working under full film lubrication. Therefore, special attention is required by the front main bearing to improve its lubrication performance under real working conditions.
MoSHuQLiuM, et al.Research on time-varying meshing stiffness of wind turbine gearbox considering tooth surface wear. Proc Inst Mech Eng Part J J Eng Tribol2023; 237: 1583–1602.
2.
KalidasPRamalingamVVMyilsamyG, et al.Numerical and experimental validation of tribological phenomenon in wind turbine brake pads using novel Archard’s wear coefficient. Proc Inst Mech Eng Part J J Eng Tribol2024; 238: 1103–1120.
3.
Encalada-DávilaÁPuruncajasBTutivénC, et al.Wind turbine main bearing fault prognosis based solely on SCADA data. Sensors2021; 21: 2228.
4.
DhanolaAGargHC. Tribological challenges and advancements in wind turbine bearings: a review. Eng Fail Anal2020; 118: 104885.
5.
HartEKeeganMMcMillanD. A lookup table approach to determining wind turbine operational fatigue loading from wind field measurements. In: ASRANet International Conference on Offshore Renewable Energy, 2016.
6.
SinghHPulikolluRVHawkinsW, et al.Investigation of microstructural alterations in low- and high-speed intermediate-stage wind turbine gearbox bearings. Tribol Lett2017; 65: 81.
7.
NicholasGHowardTLongH, et al.Measurement of roller load, load variation, and lubrication in a wind turbine gearbox high speed shaft bearing in the field. Tribol Int2020; 148: 106322.
8.
HartEClarkeBNicholasG, et al.A review of wind turbine main bearings: design, operation, modelling, damage mechanisms and fault detection. Wind Energ Sci2020; 5: 105–124.
9.
MurgiaAVerbekeRTsiporkovaE, et al.Discussion on the suitability of SCADA-based condition monitoring for wind turbine fault diagnosis through temperature data analysis. Energies2023; 16: 20.
10.
BerettaMVidalYSepulvedaJ, et al.Improved ensemble learning for wind turbine main bearing fault diagnosis. Appl Sci2021; 11: 7523.
11.
Encalada-DávilaÁMoyónLTutivénC, et al.Early fault detection in the main bearing of wind turbines based on gated recurrent unit (GRU) neural networks and SCADA data. IEEE-ASME T Mech2022; 27: 5583–5593.
12.
ZhangZ. Automatic fault prediction of wind turbine main bearing based on SCADA data and artificial neural network. Open J Appl Sci2018; 8: 211–225.
13.
StirlingJHartEKazemi AmiriA. Constructing fast and representative analytical models of wind turbine main bearings. Wind Energ Sci2021; 6: 15–31.
14.
CardaunMRoscherBSchelenzR, et al.Analysis of wind-turbine main bearing loads due to constant yaw misalignments over a 20 years timespan. Energies2019; 12: 1768.
15.
ZhengJJiJYinS, et al.Fatigue life analysis of double-row tapered roller bearing in a modern wind turbine under oscillating external load and speed. Proc Inst Mech Eng Part C J Mech Eng Sci2020; 234: 3116–3130.
16.
WuMHanXTaoY, et al.Lubrication reliability analysis of wind turbine main bearing in random wind field. Tribol Int2023; 179: 108181.
17.
HartEde MelloEDwyer-JoyceR. Wind turbine main-bearing lubrication - Part 2: simulation based results for a double-row spherical roller main-bearing in a 1.5 MW wind turbine. Wind Energ Sci Discuss2021; 2021: 1–24.
18.
BranlardEJonkmanJDanaS, et al.A digital twin based on OpenFAST linearizations for real-time load and fatigue estimation of land-based turbines. J Phys: Conf Ser2020; 1618: 022030.
19.
NoppeNWeijtjensWDevriendtC. Modeling of quasi-static thrust load of wind turbines based on 1s SCADA data. Wind Energ Sci2018; 3: 139–147.
20.
JonkmanB. Turbsim user's guide v2. 00.00. National Renewable Energy Laboratory, USA, 2014. https://doi.org/10.2172/965520
21.
JamalD. Developing a main bearing load estimation model to predict reliability of next-generation offshore wind turbines. Department of Energy, USA,2020.
22.
LundbergGPalmgrenA. Dynamic capacity of rolling bearings. J Appl Mech-T ASME1949; 16: 165–172.
23.
HartE. Developing a systematic approach to the analysis of time-varying main bearing loads for wind turbines. Wind Energy2020; 23: 2150–2165.
24.
MaFLiZQiuS, et al.Transient thermal analysis of grease-lubricated spherical roller bearings - ScienceDirect. Tribol Int2016; 93: 115–123.
25.
CaoRBaiHCaoH, et al.Mixed lubrication analysis of tapered roller bearings and crowning profile optimization based on numerical running-in method. Lubricants2023; 11: 97.
26.
WuMHanXTaoY, et al.A mixed EHL analysis method for grease and formulas for film thickness and asperity load. Tribol Lett2022; 70: 28.
27.
CenHLugtPMMorales-EspejelG. On the film thickness of grease-lubricated contacts at low speeds. Tribol Trans2014; 57: 668–678.
28.
PeiJHanXTaoY, et al.Lubrication reliability analysis of spur gear systems based on random dynamics. Tribol Int2021; 153: 106606.
29.
CaiJXiangGLiS, et al.Mathematical modeling for nonlinear dynamic mixed friction behaviors of novel coupled bearing lubricated with low-viscosity fluid. Phys Fluids2022; 34: 093612. https://doi.org/10.1063/5.0108943
30.
XiangGYangTGuoJ, et al.Optimization transient wear and contact performances of water-lubricated bearings under fluid-solid-thermal coupling condition using profile modification. Wear2022; 502-503: 204379.
31.
ChenSXiangGFillonM, et al.On the tribo-dynamic behaviors during start-up of water lubricated bearing considering imperfect journal. Tribol Int2022; 174: 107685.
32.
WangXLouZWangY, et al.An improved acoustic model for mixed lubrication contact interface. Proc Inst Mech Eng Part J J Eng Tribol2024; 238: 414–425.
33.
KellyDABarnesCG. Effects of speed on scuffing in mixed lubrication. Proc Inst Mech Eng Part J J Eng Tribol1994; 208: 269–279.
34.
HuaDShiXSunW,et al.Modeling and analysis of 3D mixed lubrication in marine cam–tappet pair. Proc Inst Mech Eng Part J J Eng Tribol2024; 238: 744–758.
35.
JonkmanJMButterfieldSMusialW, et al.Definition of a 5MW reference wind turbine for offshore system development, 2009, https://doi.org/10.2172/947422
