Effect of sample size on evaluation of variation process of rolling bearing vibration performance

被引：0
|
作者：
Ye L. [1 ]
Xia X. [2 ,3 ]
Chang Z. [4 ]
机构：
[1] School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an
[2] School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, Henan
[3] Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Henan University of Science and Technology, Luoyang, 471003, Henan
[4] Quality Inspection Center, Hangzhou Bearing Test and Research Center Company Limited, Hangzhou
来源：
Hangkong Dongli Xuebao/Journal of Aerospace Power | 2019年 / 34卷 / 11期
关键词：
Average uncertainty; Reliability evaluation; Rolling bearing; Sample size; Variation probability;
D O I：
10.13224/j.cnki.jasp.2019.11.021
中图分类号：
学科分类号：
摘要：
Based on the vibration time series during the life cycle of rolling bearings, a maximum entropy Poisson evaluation model was constructed to study the evolution process of rolling bearing vibration performance. The vibration time series were divided into different segments, and then the variation probability, vibration performance maintaining reliability and their variation speed and acceleration were calculated based on maximum entropy principle and Poisson process for each vibration time series in relation to the intrinsic series. The relationships between performance variation indexes and sample size were analyzed to select the appropriate sample size. Dynamic average uncertainty was used to analyze the uncertainty of evaluation results of performance maintaining reliability. Result showed that the sample sizes of 800-1000 and 500-900 were selected respectively, for case 1 and case 2, which can make the intrinsic series data sample contain enough vibration information, but also effectively evaluate the specific variation process of bearing vibration performance. © 2019, Editorial Department of Journal of Aerospace Power. All right reserved.
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页码：2490 / 2502
页数：12
相关论文
共 18 条
  • [1] Tang Y., Gao D., Luo G., Non-linear bearing force of the rolling ball bearing and its influence on vibration of bearing system, Journal of Aerospace Power, 21, 2, pp. 366-373, (2006)
  • [2] Wang H.C., Chen J., Performance degradation assessment of rolling bearing based on bispectrum and support vector data description, Journal of Vibration and Control, 20, 13, pp. 2032-2041, (2014)
  • [3] Nataraj C., Harsha S.P., The effect of bearing cage run-out on the nonlinear dynamics of a rotating shaft, Communications in Nonlinear Science and Numerical Simulation, 13, 4, pp. 822-838, (2008)
  • [4] Ye L., Xia X.T., Chang Z., Dynamic prediction for accuracy maintaining reliability of super-precision rolling bearing in service, Shock and Vibration, 2018, pp. 1-15, (2018)
  • [5] Xia X., Ye L., Chang Z., Et al., Prediction for variation process of reliability on vibration performance of rolling bearings under the condition of poor information, Journal of Vibration and Shock, 36, 8, pp. 105-112, (2017)
  • [6] Xia X., Meng Y., Qiu M., Forecasting for variation process of reliability of rolling bearing vibration performance using grey bootstrap Poisson method, Journal of Mechanical Engineering, 51, 9, pp. 97-103, (2015)
  • [7] Zhang R., Wang Y., Guo A., Impact of sample uncertainty on SPI index based drought evaluation, Journal of Northwest Agriculture and Forestry University(Natural Science Edition), 17, 11, pp. 1-10, (2019)
  • [8] Wu L., Liu S., Yao L., Discrete grey model based on fractional order accumulate, Systems Engineering Theory and Practice, 34, 7, pp. 1822-1827, (2014)
  • [9] Chen X., Lei Y., Zhang X., Et al., Effects of sample sizes on accuracy and stability of maximum entropy model in predicting species distribution, Scientia Silvae Sinicae, 48, 1, pp. 53-59, (2012)
  • [10] Edwin A.B., Angel K.M., A clustering method based on the maximum entropy principle, Entropy, 17, 1, pp. 151-180, (2015)
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