Remaining life predictions of rolling bearing based on relative features and multivariable support vector machine

被引：43
|
作者：
机构：
[1] State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University
来源：
Chen, X. (chenxf@mail.xjtu.edu.cn) | 1600年 / Chinese Mechanical Engineering Society卷 / 49期
关键词：
Degradation assessment; Multivariable support vector machine(MSVM); Relative root mean square; Remaining life prediction;
D O I：
10.3901/JME.2013.02.183
中图分类号：
学科分类号：
摘要：
Novel prediction method is proposed based on the relative features and multivariable support vector machine (MSVM) to estimate the rolling bearing remaining life under limited condition data. The relative root mean square (RRMS) with ineffectiveness of the bearing individual difference is used to assess the performance degradation, and sensitive features are selected as input by correlation analysis. Meanwhile, MSVM is structured to predict the remaining life, which has the advantages of multivariable prediction and the small samples prediction. Unlike univariate SVM, MSVM overcomes the simple structure and the lack of information, and excavates the potential information of small sample as much as possible. The simulation and the bearing run-to-failure tests are carried out to inspect the prediction model, and the results demonstrate that MSVM can utilize the effective information as much as possible for the more precise results with the practical values and generality. © 2013 Journal of Mechanical Engineering.
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页码：183 / 189
页数：6
相关论文
共 16 条
  • [1] Zhang X., Chen X., Li B., Et al., Review of life prediction for mechanical major equipments, Journal of Mechanical Engineering, 47, 11, pp. 100-116, (2011)
  • [2] Lei Y., He Z., Zi Y., Fault diagnosis based on novel hybrid intelligent model, Chinese Journal of Mechanical Engineering, 44, 7, pp. 112-117, (2008)
  • [3] Shao Y., Nezu K., Prognosis of remaining bearing life using neural networks, Journal of Systems and Control Engineering, 214, 13, pp. 217-230, (2000)
  • [4] Gebraeel N., Lawley M., Liu R., Et al., Residual life predictions from vibration- based degradation signals: A neral network approach, IEEE Tran. on Induxttrial Electronics, 51, 3, pp. 694-700, (2004)
  • [5] Huang R., Xi L., Li X., Et al., Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods, Mechanical Systems and Signal Processing, 21, pp. 193-207, (2007)
  • [6] Xi L., Huang R., Li X., Et al., Residual life predictions for ball bearing based on neural networks, Chinese Journal of Mechanical Engineering, 43, 10, pp. 137-143, (2007)
  • [7] Pan Y., Chen J., Li X., Et al., Spectral entropy: A complementary index for rolling element bearing performance degradation assessment, Journal of Mechanical Engineering System, 223, pp. 1223-1231, (2009)
  • [8] Zaretsky E.V., Poplawski J.V., Peters S.M., Comparision of life theories for rolling element bearing, Trib Trans., 39, 2, pp. 237-248, (1996)
  • [9] Li Y., Billigton S., Zhang C., Et al., Adaptive prognostics for rolling element bearing condition, Mechanical Systems and Signal Processing, 13, 1, pp. 103-113, (1999)
  • [10] Vapnik V.N., Statistical Learning Theory, (1998)
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