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.
黄晨光1,2,张兵1,易彩1,靳行1. 高速列车轴箱轴承多故障滚动体振动模型及其缺陷定位方法[J]. 振动与冲击, 2020, 39(18): 34-43.
HUANG Chenguang1,2,ZHANG Bing1,YI Cai1,JIN Hang1. Vibration model for axle box bearings with multiple defective rolling elements for high-speed trains and the defects localization method. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(18): 34-43.
[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.