涡旋压缩机滚动轴承疲劳寿命分析及可靠性研究
Fatigue Life Analysis and Reliability Study of Rolling Bearings in Scroll Compressor
DOI: 10.12677/mos.2024.132161, PDF,   
作者: 何伟兴, 卢 曦:上海理工大学机械工程学院,上海;卢 诚:上海海立新能源技术有限公司,上海
关键词: 滚动轴承涡旋压缩机疲劳寿命可靠性Rolling Bearing Scroll Compressor Fatigue Life Reliability
摘要: 针对某电动汽车涡旋压缩机动盘轴承容易发生疲劳失效的问题,研究了不同转速、不同径向载荷以及两者相互匹配对其疲劳寿命的影响。对压缩机转子结构进行受力分析,计算动盘轴承在不同工况下受到的径向载荷。使用UG对动盘轴承进行建模,联合WORKBENCH/LS-DYNA和nCode Designlife对不同工况下的轴承进行疲劳仿真分析,得到轴承的应力分布、损伤情况和寿命分布。并针对不同工况进行台架试验以验证仿真结果的合理性。结果表明疲劳失效主要发生在轴承承载区滚子与内外圈滚道接触的位置,转速与径向载荷对动盘轴承疲劳寿命的影响并非简单的线性关系,且径向载荷的影响大于转速,仿真结果与试验结果平均绝对值误差为8.7%,大幅降低了动盘轴承疲劳寿命试验的时间成本,对涡旋压缩机使用寿命的研发具有一定的参考意义。
Abstract: Regarding the issue of fatigue failure in the bearings of a certain electric vehicle’s scroll compressor rotor, the effects of different rotational speeds, radial loads, and their matching on their fatigue life were studied. Perform force analysis on the compressor rotor structure and calculate the radial load on the rotating turbine bearing under different working conditions. Model the rotating turbine bearing using UG, combined with WORKBENCH/LS-DYNA and nCode Designlife for fatigue simulation analysis of bearings under different working conditions, to obtain the stress distribution, damage situation, and life distribution of the bearings. And conduct bench tests for different operating conditions to verify the rationality of the simulation results. The results indicate that fatigue failure occurs mainly in the bearing load area where the rollers are in contact with the raceways of the inner and outer rings. The influence of rotational speed and radial load on the fatigue life of the rotating turbine bearing is not a simple linear relationship, and the influence of radial load is greater than that of rotational speed. The average absolute error between simulation results and experimental results is 8.7%, which significantly reduces the time cost of fatigue life testing for rotating turbine bearings. The research and development of scroll compressor service life has certain reference significance.
文章引用:何伟兴, 卢曦, 卢诚. 涡旋压缩机滚动轴承疲劳寿命分析及可靠性研究[J]. 建模与仿真, 2024, 13(2): 1705-1716. https://doi.org/10.12677/mos.2024.132161

参考文献

[1] Lei, Y., Li, N., Guo, L., et al. (2018) Machinery Health Prognostics: A Systematic Review from Data Acquisition to RUL Prediction. Mechanical Systems and Signal Processing, 104, 799-834. [Google Scholar] [CrossRef
[2] 何正嘉, 曹宏瑞, 訾艳阳, 等. 机械设备运行可靠性评估的发展与思考[J]. 机械工程学报, 2014, 50(2): 171-186.
http://www.cjmenet.com.cn/CN/Y2014/V50/I2/171
[3] Lei, Y., Han, T., Wang, B., et al. (2019) XJTU-SY Rolling Element Bearing Accelerated Life Test Datasets: A Tutorial. Journal of Mechanical Engineering, 55, 1-6. [Google Scholar] [CrossRef
[4] 涂文兵, 杨锦雯, 罗丫, 等. 滚动轴承故障的显式动力学仿真与接触特性分析[J]. 机械设计与研究, 2019, 35(3): 75-79. [Google Scholar] [CrossRef
[5] Ren, L., Sun, Y., Wang, H., et al. (2018) Prediction of Bearing Remaining Useful Life with Deep Convolution Neural Network. IEEE Access, 6, 13041-13049. [Google Scholar] [CrossRef
[6] Kou, W., Yang, L. and Hong, Y. (2015) Dynamic Simulation of Infrared Signature of Deep Groove Ball Bearing Based on ANSYS/LS-DYNA. http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2199807 [Google Scholar] [CrossRef
[7] 张永超, 杨海昆, 刘嵩寿, 等. 基于RBM-CNN模型的滚动轴承剩余寿命预测方法[J/OL]. 轴承, 2023: 1-7.
https://kns.cnki.net/kcms2/article/abstract?v=b4E8SuETvlJYF8etZ9fImaYeUyHM05T_PPAKACEGlfNSDnlDIrC9eGqj1iyiAK1FYfMhVpuZJHv7Uh6OZmb0gKr-4rGAumXQOArNwppK2ZSntleuMuNYkn6Ozxs73nxsVf9APE8Xoco=&uniplatform=NZKPT&language=CHS, 2024-03-27.
[8] 胡腾, 殷国富, 孙明楠. 基于离心力和陀螺力矩效应的“主轴-轴承”系统动力学特性研究[J]. 振动与冲击, 2014, 33(8): 100-108. [Google Scholar] [CrossRef
[9] Wang, Y., Zhang, G., Ma, C., et al. (2019) Explicit Dynamics Simulation of High-Speed Railway Bearing Based On ANSYS/LS-DYNA. IOP Conference Series: Materials Science and Engineering, 612, Article 032011. [Google Scholar] [CrossRef
[10] 滕文博, 鲜桂城, 董霖, 等. 基于Romax的薄壁深沟球轴承寿命研究[J]. 机械设计, 2021, 38(1): 85-89. [Google Scholar] [CrossRef
[11] Deutsch, J., He, M. and He, D. (2017) Remaining Useful Life Prediction of Hybrid Ceramic Bearings Using an Integrated Deep Learning and Particle Filter Approach. Applied Sciences, 7, 649. [Google Scholar] [CrossRef
[12] 魏延刚, 段同江, 姚金池, 等. 内外圈相对倾斜对圆柱滚子轴承寿命的影响[J]. 制造技术与机床, 2024(1): 136-142. [Google Scholar] [CrossRef
[13] Zhang, N., Wu, L., Wang, Z., et al. (2018) Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions. Entropy, 20, 944. [Google Scholar] [CrossRef] [PubMed]
[14] Ren, H., Zhou, A., Jia, J., et al. (2023) Fatigue Life Analysis of Bearing in Oscillatory Applications Based on Ncode DesignLife. Journal of Physics: Conference Series, 2489, Article 012027. [Google Scholar] [CrossRef
[15] 涂文兵, 张桂源, 罗丫, 等. 基于滑移非理想赫兹接触的滚动轴承振动特性分析[J]. 振动与冲击, 2023, 42(5): 30-38. [Google Scholar] [CrossRef