针对传统时域分析方法识别滚动轴承故障滚动体数量和相位信息容易失效的问题,建立了存在多个故障滚动体的滚动轴承振动模型,并提出了基于包络谱和卷积平均思想的故障滚动体定位方法。所提模型综合考虑了包括轴承几何结构、轴转速、轴承载荷分布、传递函数、振动的指数衰减和滚动体随机滑动等多个因素。结合所提模型,推导出不同滚动体缺陷激发的最大冲击的时间间隔受缺陷在滚动体上的位置分布的影响,导致该时间间隔存在较大波动。阐述了传统时域分析中,采用最大冲击间隔定位故障滚动体容易失效的原因。应用高速列车轴箱轴承试验数据验证了所提模型的准确性和所提缺陷定位方法的有效性,结果表明,所提模型对理解滚动体故障轴承的振动机理和对设计具体的分析和诊断工具有所帮助,所提缺陷定位方法能有效识别故障滚动体的数量和间隔信息,相比传统时域分析方法,缺陷定位的效率和抗噪声干扰能力得到了显著提高。
Abstract
Aiming at improving the shortcomings of the traditional time-domain analysis method in locating the defective rolling elements, the vibration model of rolling bearing with multiple defective rolling elements is established, and then the defects localization methods based on envelope spectrum and convolution average are proposed. The effects of bearing geometry, shaft speed, bearing load distribution, transfer function, exponential decay of vibration and the random slip of rolling elements and cage are taken into account for the vibration model. Combined with the model, it is found that the time interval between the maximum impulses produced by different rolling element defects is affected by the position distribution of the defects on the rolling elements. Then the invalidation of the time-domain analysis method has been described. The high-speed train axle box bearing test data is used to verify the accuracy of the proposed model and the effectiveness of the proposed defects localization methods. The results show that the proposed model is helpful in understanding the vibration mechanism of the faulty bearing and in designing the specific analysis diagnostic tools, and that the proposed defects localization methods can effectively extract the number and interval information of the defective rolling elements. Compared with the traditional time-domain analysis method, the efficiency of defects localization and the ability to resist noise are significantly improved.
关键词
轴箱轴承 /
振动模型 /
多故障滚动体 /
缺陷定位 /
卷积平均
{{custom_keyword}} /
Key words
Axle box bearing /
Vibration model /
Multiple defective rolling elements /
Defect localization /
Convolution average
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Jianming Ding. Fault detection of a wheelset bearing in a high-speed train using the shock-response convolutional sparse-coding technique[J]. Measurement, 2018, 117:108-124.
[2] P.D. McFadden, J.D. Smith. Model for the vibration produced by a single point defect in a rolling element bearing[J]. Journal of Sound and Vibration, 1984, 96(1):69-82.
[3] P.D. McFadden, J.D. Smith. The vibration produced by multiple point defects in a rolling element bearing[J]. Journal of Sound and Vibration, 1985, 98(2):263-273.
[4] Jérôme Antoni, Robert B. Randall. Differential diagnosis of gear and bearing faults[J]. ASME Journal of Vibration and Acoustics, 2002, 124:165–171.
[5] Jérôme Antoni, Robert B. Randall. A stochastic model for simulation and diagnostics of rolling element bearings with localised faults[J]. ASME Journal of Vibration and Acoustics, 2003, 125:282–289.
[6] Feiyun Cong, Jin Chen, Guangming Dong, et al. Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis[J]. Journal of Sound and Vibration, 2013, 332(8):2081-2097.
[7] Daniel Maraini, C. Nataraj. Nonlinear analysis of a rotor-bearing system using describing functions[J]. Journal of Sound and Vibration, 2018, 420:227-241.
[8] Peng Gao, Lei Hou, Rui Yang, et al. Local defect modelling and nonlinear dynamic analysis for the inter-shaft bearing in a dual-rotor system, [J]. Applied Mathematical Modelling, 2019, 68:29-47.
[9] Aoyu Chen, Thomas R. Kurfess. A new model for rolling element bearing defect size estimation[J]. Measurement, 2018, 114:144-149.
[10] Aoyu Chen, Thomas R. Kurfess. Signal processing techniques for rolling element bearing spall size estimation[J]. Mechanical Systems and Signal Processing, 2019, 117:16-32.
[11] 黄文涛, 董振振, 孔繁朝. 引入撞击力的滚动轴承内圈故障振动模型[J]. 振动与冲击, 2016, 35(17):121-126,159.
HUANG Wentao, DONG Zhenzhen, KONG Fanchao. Vibration model of rolling element bearings with inner race faults considering impulse force[J]. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(17):121-126,159.
[12] 赵方伟. 高速动车组轴箱轴承接触模型的建立与分析[J]. 轴承, 2019(3): 1-6.
ZHAO Fangwei. Establishment and Analysis on Contact Model of Axle Box Bearings for High Speed EMU[J]. Bearing, 2019(3): 1-6.
[13] V.N. Patel, N. Tandon, R.K. Pandey. Vibrations Generated by Rolling Element Bearings having Multiple Local Defects on Races[J]. Procedia Technology, 2014, 14:312-319.
[14] Robert B. Randall, Jérôme Antoni. Rolling element bearing diagnostics—A tutorial[J]. Mechanical Systems and Signal Processing, 2011, 25(2):488-520.
[15] Tedric A. Harris, Michael N. Kotzalas. Advanced Concepts of Bearing Technology, Rolling Bearing Analysis[M]. Fifth Edition. Boca Raton: CRC Press, 2006:25.
[16] D. Ho, R. B. Randall. Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals[J]. Mechanical Systems and Signal Processing, 2000, 14(5):763-788.
[17] Chenguang Huang, Jianhui Lin, Jianming Ding, et al. A Novel Wheelset Bearing Fault Diagnosis Method Integrated CEEMDAN, Periodic Segment Matrix, and SVD[J]. Shock and Vibration, 2018: 1-18.
[18] Dong Wang. Some further thoughts about spectral kurtosis, spectral L2/L1 norm, spectral smoothness index and spectral Gini index for characterizing repetitive transients[J]. Mechanical Systems and Signal Processing, 2018, 108(1):360-368.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}