基于齿廓修形和摩擦耦合的风电齿轮磨损动力学特性分析

doi:10.19912/j.0254-0096.tynxb.2021-1280

摘要/Abstract

摘要： 为定量研究齿面磨损（TSW）的振动特性,提出一种基于齿廓修形和摩擦耦合的磨损故障建模方法,并引入最大磨损深度来表征齿面磨损程度。以大型风电机组齿轮箱高速输出轴直齿轮为研究对象,得到对应不同磨损程度下的齿轮时变啮合刚度,并将其代入到齿轮箱8自由度动力学微分方程,在不同转矩条件下对齿轮箱进行动力学仿真,得到不同磨损严重程度和转矩条件下的振动时频域特性。结果表明：仿真结果与实验数据吻合良好,验证了该方法的有效性;随着齿面磨损程度增加、转矩增大,齿轮箱振动、齿轮扭振剧烈;在0.4倍额定转矩附近,齿面磨损引起齿轮箱振动速度、振动加速度最为明显。研究结果可为齿轮磨损故障诊断提供理论指导。

关键词: 风电机组, 齿轮箱, 动力学, 摩擦, 时变啮合刚度, 磨损, 齿廓修形

Abstract: In order to quantitatively study the vibration characteristics of tooth surface wear （TSW）, a wear fault modeling method based on tooth profile modification and friction coupling is proposed, and the maximum wear depth is introduced to characterize the degree of tooth surface wear. The high-speed output shaft spur gear of the large-scale wind turbine gearbox is taken as the research object, the time-varying meshing stiffness of the gear corresponding to different degrees of wear is obtained, and it is substituted into the dynamic differential equation of the gearbox with 8 degrees of freedom. The vibration time-frequency characteristics under different wear severity and torque conditions are obtained after gearbox dynamic simulation. The results show that the simulation results are in good agreement with the relevant experimental data, which verifies the effectiveness of the method. The gearbox and the pinion vibrate more severely with the increase of tooth surface wear degree and torque. The vibration and impact load of the gearbox caused by tooth surface wear are the most obvious near 40% rated torque, which provides theoretical guidance for gear wear fault diagnosis.

Key words: wind turbines, gear box, dynamics, friction, time varying meshing stiffness, wear, tooth profile modification

中图分类号:

TK83

田德, 陶立壮, 胡玥, 李贝. 基于齿廓修形和摩擦耦合的风电齿轮磨损动力学特性分析[J]. 太阳能学报, 2022, 43(5): 260-269.

Tian De, Tao Lizhuang, Hu Yue, Li Bei. DYNAMICS OF WIND TURBINE GEAR WEAR FAULT BASED ON TOOTH PROFILE MODIFICATION AND FRICTION COUPLING[J]. Acta Energiae Solaris Sinica, 2022, 43(5): 260-269.

参考文献

[1] XIANG L, GAO N, HU A.Dynamic analysis of a planetary gear system with multiple nonlinear parameters[J]. Journal of computational and applied mathematics, 2018, 327: 325-340.
[2] LIANG M X, WANG Y, ZHAO T.Optimization on nonlinear dynamics of gear rattle in automotive transmission system[J]. Shock and vibration, 2019, 2019: 1-12.
[3] SHI X J, SUN W, LU X Q, et al.Three-dimensional mixed lubrication analysis of spur gears with machined roughness[J]. Tribology international, 2019, 140: 105864.
[4] XU X Y, LAI J B, LOHMANN C, et al.A model to predict initiation and propagation of micro-pitting on tooth flanks of spur gears[J]. International journal of fatigue, 2019, 122: 106-115.
[5] RAADNUI S. Spur gear wear analysis as applied for tribological based predictive maintenance diagnostics[J]. Wear, 2019, 426-427: 1748-1760.
[6] AMARNATH M, LEE S.Assessment of surface contact fatigue failure in a spur geared system based on the tribological and vibration parameter analysis[J]. Measurement, 2015, 76: 32-44.
[7] CHANG H C, BORGHESANI P, PENG Z X.Automated assessment of gear wear mechanism and severity using mould images and convolutional neural networks[J]. Tribology international, 2020, 147: 106280.
[8] CHEN W, LEI Y L, FU Y, et al.A study of effects of tooth surface wear on time-varying mesh stiffness of external spur gear considering wear evolution process[J]. Mechanism and machine theory, 2021, 155: 104055.
[9] 张俊, 卞世元, 鲁庆, 等. 准静态工况下渐开线直齿轮齿面磨损建模与分析[J]. 机械工程学报, 2017, 53(5): 136-145.
ZHANG J, BIAN S Y, LU Q, et al.Quasi-static-model-based wear analysis of spur gears[J]. Journal of mechanical engineering, 2017, 53(5): 136-145.
[10] FENG K, BORGHESANI P, SMITH W A, et al. Vibration-based updating of wear prediction for spur gears[J]. Wear, 2019, 426-427: 1410-1415.
[11] WU S Y, ZUO M J, PAREY A.Simulation of spur gear dynamics and estimation of fault growth[J]. Journal of sound and vibration, 2008, 317(3-5): 608-624.
[12] 冯松, 毛军红, 谢友柏. 齿面磨损对齿轮啮合刚度影响的计算与分析[J]. 机械工程学报, 2015, 51(15): 27-32.
FENG S, MAO J H, XIE Y B.Analysis and calculation of gear mesh stiffness with tooth wear[J]. Journal of mechanical engineering, 2015, 51(15): 27-32.
[13] ZHANG H S, PAN J, RAO M, et al.A detailed investigation of gear body-induced tooth deflections and development of an improved analytical solution[J]. Applied sciences, 2020, 10(7): 2292.
[14] PRABHU R, SATHISHKUMAR R. Enhancement of wear resistance on normal contact ratio spur gear pairs through non-standard gears[J]. Wear, 2017, 380-381: 228-239.
[15] 杨勇, 王家序, 周青华, 等. 考虑摩擦的磨损和修形齿轮啮合刚度计算[J]. 工程科学与技术, 2018, 50(2): 212-219.
YANG Y, WANG J X, ZHOU Q H, et al.Stiffness calculation considering friction for a spur gear pair with tooth wear and profile modification[J]. Advanced engineering sciences, 2018, 50(2): 212-219.
[16] BAGLIONI S, CIANETTI F, LANDI L.Influence of the addendum modification on spur gear efficiency[J]. Mechanism and machine theory, 2012, 49: 216-233.
[17] GU Y K, LI W F, ZHANG J, et al.Effects of wear, backlash, and bearing clearance on dynamic characteristics of a spur gear system[J]. IEEE access, 2019, 117(7): 639-651.
[18] 任望, 刘杰, 赵伟强. 齿面磨损对齿轮系统动态特性的影响[J]. 润滑与密封, 2019, 44(11): 67-72.
REN W, LIU J, ZHAO W Q.Effect of tooth surface wear on dynamic characteristics of gear system[J]. Lubrication engineering, 2019, 44(11): 67-72.
[19] WANG Q B, LI Z W, MA H, et al.Effects of different coupling models of a helical gear system on vibration characteristics[J]. Journal of mechanical science and technology, 2017, 31(5): 2143-2154.
[20] PEETERS J L M, VANDEPITTE D, SAS P. Analysis of internal drive train dynamics in a wind turbine[J]. Wind energy, 2006, 9(1-2): 141-161.
[21] ZHAI H F, ZHU C C, SONG C S, et al.Dynamic modeling and analysis for transmission system of high-power wind turbine gearbox[J]. Journal of mechanical science and technology, 2015, 29(10): 4073-4082.
[22] HE G L, DING K, WU X M, et al.Dynamics modeling and vibration modulation signal analysis of wind turbine planetary gearbox with a floating sun gear[J]. Renewable energy, 2019, 139: 718-729.
[23] LIANG X, ZUO M J, FENG Z.Dynamic modeling of gearbox faults: A review[J]. Mechanical systems and signal processing, 2018, 98: 852-876.
[24] SHAO S Y, MCALEER S, YAN R Q, et al.Highly accurate machine fault diagnosis using deep transfer learning[J]. IEEE transactions on industrial informatics, 2019, 15(4): 2446-2455.