针对以往风电机组轴承温度发热端建模简化和未考虑实际环境影响,难以建立准确的轴承温度模型的问题,提出基于数字孪生的轴承温度动态预测方法。首先,采用有限元法建立系统动力学模型,初步得到轴承振动加速度;其次,通过热网络法构建轴承温度计算机理模型。借助优化算法建立基于实测数据为目标的轴承振动优化模型,并利用实验数据和仿真结果构建滚动轴承热边界条件在线修正的数字孪生模型,用数字孪生优化后动的力学模型更新热网络仿真温度。试验结果表明,在变转速的工况下,该模型在振动加速度估计精度和轴承温度预测精度具备良好表现,可有效提高轴承热网络分析的精度和预测模型的准确性。
Abstract
In order to solve the problems that the modeling of the heating end of the bearing temperature of a wind turbine was simplified in the past and the actual environment was not considered, making it difficult to establish an accurate bearing temperature model, a dynamic prediction method of bearing temperature based on a digital twin is proposed. Firstly, the system dynamics model is established by the finite element method, and the bearing vibration acceleration is preliminarily obtained. Secondly, the mechanism model of bearing temperature calculation is constructed by the thermal network method. The bearing vibration optimization model based on the measured data is established by the optimization algorithm, and the digital twin model of the rolling bearing's thermal boundary conditions is constructed by using the experimental data and simulation results. The dynamic model after the digital twin optimization is used to update the thermal network simulation of temperature. The test results show that the model has good performance in vibration acceleration estimation accuracy and bearing temperature prediction accuracy under the condition of variable speed, which can effectively improve the accuracy of bearing thermal network analysis and prediction model.
关键词
数字孪生 /
轴承 /
风力发电机 /
有限元法 /
温度预测 /
动态优化
Key words
digital twin /
bearing /
wind turbine /
finite element method /
temperature prediction /
dynamic optimization
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参考文献
[1] 邱星辉, 韩勤锴, 褚福磊. 风力机行星齿轮传动系统动力学研究综述[J]. 机械工程学报, 2014, 50(11): 23-36.
QIU X H, HAN Q K, CHU F L.Review on dynamic analysis of wind turbine geared transmission systems[J]. Journal of mechanical engineering, 2014, 50(11): 23-36.
[2] 乔帅, 周建星, 章翔峰, 等. 风电机组中两级齿轮传动系统动态温度响应与实验研究[J]. 太阳能学报, 2022, 43(1): 116-124.
QIAO S, ZHOU J X, ZHANG X F, et al.Dynamic temperature response and experimental study of two-stage gear transmission system in wind turbine[J]. Acta energiae solaris sinica, 2022, 43(1): 116-124.
[3] 马璇, 李建华, 陈国定. 减速器齿轮传动系统的稳态热分析及试验研究[J]. 西北工业大学学报, 2002, 20(1): 32-35.
MA X, LI J H, CHEN G D.Experimentally supported stable thermal analysis of Gearbox transmission system[J]. Journal of Northwestern Polytechnical University, 2002, 20(1): 32-35.
[4] 程庆元, 陆凤霞, 鲍和云. 基于热网络法与有限元法的球轴承稳态热分析[J]. 机械制造与自动化, 2013, 42(6): 97-100.
CHENG Q Y, LU F X, BAO H Y.Steady state thermal analysis of ball bearing based on thermal network method and the finite element method[J]. Machine building & automation, 2013, 42(6): 97-100.
[5] GRIEVES M.Digital twin manufacturing excellence through virtual factory replication[J]. White paper, 2014, 1: 1-7.
[6] TAO F, CHENG J F, QI Q L, et al.Digital twin-driven product design, manufacturing and service with big data[J]. The international journal of advanced manufacturing technology, 2018, 94(9): 3563-3576.
[7] 徐建江, 尹习双, 刘金飞, 等. 基于数字孪生的风电建设智能协同管控技术研究及应用[J]. 太阳能学报, 2024, 45(6): 536-543.
XU J J, YIN X S, LIU J F, et al.Research and application of intelligent collaborative control technology for wind power construction based on digital twin[J]. Acta energiae solaris sinica, 2024, 45(6): 536-543.
[8] 尚海勇, 刘利强, 齐咏生, 等. 基于数字孪生技术的风电机组建模研究[J]. 太阳能学报, 2023, 44(5): 391-400.
SHANG H Y, LIU L Q, QI Y S, et al.Research on wind turbine modeling based on digital twin technology[J]. Acta energiae solaris sinica, 2023, 44(5): 391-400.
[9] XIAO J Y, FAN K G.Research on the digital twin for thermal characteristics of motorized spindle[J]. The international journal of advanced manufacturing technology, 2022, 119(7): 5107-5118.
[10] 徐荣飞, 范开国. 基于数字孪生的电主轴热特性研究[J]. 中国机械工程, 2022, 33(16): 1965-1971.
XU R F, FAN K G.Research on thermal characteristics of motorized spindles based on digital twin[J]. China mechanical engineering, 2022, 33(16): 1965-1971.
[11] YUAN Y Q, FAN K G.Digital twin-driven life health monitoring for motorized spindle[J]. Journal of manufacturing processes, 2024, 113: 373-387.
[12] WANG Y, CAO J C, TONG Q B, et al.Study on the thermal performance and temperature distribution of ball bearings in the traction motor of a high-speed EMU[J]. Applied sciences, 2020, 10(12): 4373.
[13] QIN Y, WU X G, LUO J.Data-model combined driven digital twin of life-cycle rolling bearing[J]. IEEE transactions on industrial informatics, 2021, 18(3): 1530-1540.
[14] 王胜男, 阿达依·谢尔亚孜旦, 章翔峰, 等. 考虑轴柔性的二级齿轮减速器振动噪声研究[J]. 西安交通大学学报, 2020, 54(9): 117-127.
WANG S N, ADAYI X, ZHANG X F, et al.Vibration and noise analysis for two-stage gear reducer considering shaft flexibility[J]. Journal of Xi’an Jiaotong University, 2020, 54(9): 117-127.
[15] 乔自珍, 周建星, 章翔峰. 多源时变激励下两级直齿轮传动系统有限元建模方法研究[J]. 振动与冲击, 2019, 38(15): 182-189.
QIAO Z Z, ZHOU J X, ZHANG X F.Finite element modelling method for a two-stage spur gear transmission system under multi-source time-varying excitations[J]. Journal of vibration and shock, 2019, 38(15): 182-189.
[16] HARRIS T A.Rolling bearing analysis[M]. Crc Press, 2006.
[17] ZHOU Y D, ZHOU J X, CUI Q W, et al.Digital twin-driven online intelligent assessment of wind turbine gearbox[J]. Wind energy, 2024, 27(8): 797-815.
[18] MIRJALILI S Z, MIRJALILI S, SAREMI S, et al.Grasshopper optimization algorithm for multi-objective optimization problems[J]. Applied intelligence, 2018, 48(4): 805-820.
[19] SAREMI S, MIRJALILI S, LEWIS A.Grasshopper optimisation algorithm: theory and application[J]. Advances in engineering software, 2017, 105: 30-47.
[20] 乔帅. 风电机组中两级齿轮传动系统动态温度响应与实验研究[D]. 乌鲁木齐: 新疆大学, 2021.
QIAO S.Dynamic temperature response and experimental research of two-stage gear transmission system in wind turbine[D]. Urumqi: Xinjiang University, 2021.
[21] 周军香, 周元坤, 刘佳. 滚动轴承振动与温度数据序列的灰关系分析与自助评估[J]. 轴承, 2020(10): 30-36.
ZHOU J X, ZHOU Y K, LIU J.Grey relational analysis and bootstrap evaluation on data series of vibration and temperature of rolling bearings[J]. Bearing, 2020(10): 30-36.
基金
新疆维吾尔自治区重点研发项目（2022B01017-1）; 昌吉州科技计划项目（2023Z02）; 新疆维吾尔自治区科技发展计划项目（2022LQ03015）
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