Thermal error prediction and reliability analysis of the main shaft bearing at wind turbines

被引:0
|
作者
Zhang, Peng [1 ]
Jiang, Zhiyuan [1 ]
Huang, Xianzhen [1 ,2 ,4 ]
Wang, Yuping [1 ]
Rong, Zhiming [3 ,5 ]
机构
[1] Northeastern Univ, Sch Mech Engn & Automat, Shenyang, Peoples R China
[2] Northeastern Univ, Key Lab Vibrat & Control Aeroprop Syst, Minist Educ China, Shenyang, Peoples R China
[3] Dalian Ocean Univ, Apllied Technol Coll, Dalian, Peoples R China
[4] Northeastern Univ, 11 Lane 3,WenHua Rd, Shenyang 110819, Peoples R China
[5] Dalian Ocean Univ, Dalian, Liaoning, Peoples R China
基金
中国国家自然科学基金;
关键词
Main shaft bearing; reliability analysis; assembly preload; thermal error; stochastic configuration network; STOCHASTIC CONFIGURATION NETWORKS; HIGH-SPEED SPINDLE; MODEL; CAPACITY; FAILURE;
D O I
10.1177/09544062241256500
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
During the operation of the gearless wind turbine, the phenomenon of heat generation in the main shaft bearing is inevitable and further affects the assembly preload. It is crucial to determine the effect of the thermal error on bearing assembly preload. In this paper, a reliability analysis method for main shaft bearing is proposed. Firstly, a finite element model for the thermal analysis of wind turbines is established based on heat transfer theory, and the thermal error of the preload is calculated. Subsequently, a reliability analysis of the main shaft bearing is conducted through Quasi-Monte Carlo simulation (QMCS) considering the influence of uncertainty factors. To further improve the computational efficiency, a surrogate model based on stochastic configuration network (SCN) is established to analyze the reliability and sensitivity of the bearing. Finally, the numerical example shows that the proposed model has high accuracy and applicability.
引用
收藏
页码:9773 / 9792
页数:20
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