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Shock and Vibration
Volume 2018, Article ID 3013684, 10 pages
https://doi.org/10.1155/2018/3013684
Research Article

Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model

School of Mechanical Engineering, Dalian University of Technology, Dalian, China

Correspondence should be addressed to Fengtao Wang; nc.ude.tuld@tfgnaw

Received 18 January 2018; Revised 16 May 2018; Accepted 21 June 2018; Published 8 July 2018

Academic Editor: Mohammad A. Hariri-Ardebili

Copyright © 2018 Fengtao Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. L. Cui, J. Huang, and F. Zhang, “Quantitative and localization diagnosis of a defective ball bearing based on vertical-horizontal synchronization signal analysis,” IEEE Transactions on Industrial Electronics, vol. 64, no. 11, pp. 8695–8706, 2017. View at Publisher · View at Google Scholar · View at Scopus
  2. L. Cui, N. Wu, C. Ma, and H. Wang, “Quantitative fault analysis of roller bearings based on a novel matching pursuit method with a new step-impulse dictionary,” Mechanical Systems and Signal Processing, vol. 68-69, pp. 34–43, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. J. He and Z. Y. Huang, Selection of Mechanical Fault Diagnosis, Xi’an Jiaotong University Press, Xian, China, 1991.
  4. H. P. Zhuo, J. Cai, and H. W. Wang, The Theory and Methods of Maintenance Strategies, Aviation Industry Press, Beijing, China, 2008.
  5. H. F. Zuo, H. J. Li, and X. Rong, “Condition based aero-engine maintenance decisionmethod using proportional hazards model,” Journal of Aerospace Power, vol. 21, pp. 716–721, 2006. View at Google Scholar
  6. W. Guo and P. W. Tse, “A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals,” Journal of Sound and Vibration, vol. 332, no. 2, pp. 423–441, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. F. Cong, J. Chen, G. Dong, and M. Pecht, “Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis,” Journal of Sound and Vibration, vol. 332, no. 8, pp. 2081–2097, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. D. R. Cox, “Regression models and life-tables,” Journal of the Royal Statistical Society, vol. 34, pp. 187–220, 1972. View at Google Scholar · View at MathSciNet
  9. F. Ding, “Reliability Assessment Based on Equipment Condition Vibration Feature Using Proportional Hazards Model,” Journal of Mechanical Engineering, vol. 45, no. 12, p. 89, 2009. View at Publisher · View at Google Scholar
  10. R. Zimroz, W. Bartelmus, T. Barszcz, and J. Urbanek, “Diagnostics of bearings in presence of strong operating conditions non-stationarity - A procedure of load-dependent features processing with application to wind turbine bearings,” Mechanical Systems and Signal Processing, vol. 46, no. 1, pp. 16–27, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Liao, W. Zhao, and H. Guo, “Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model,” in Proceedings of the Annual Reliability and Maintainability Symposium, pp. 127–132, Newport Beach, Calif, USA, January 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. Q. Zhang, C. Hua, and G. H. Xu, “A mixture Weibull proportional hazard model for mechanical system failure prediction utilizing lifetime and monitoring,” Mechanical Systems Signal Processing, vol. 43, pp. 103–112, 2014. View at Publisher · View at Google Scholar
  13. C. K. R. Lim and D. Mba, “Switching Kalman filter for failure prognostic,” Mechanical Systems and Signal Processing, vol. 52-53, no. 1, pp. 426–435, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. J. L. Deng, “A review of grey system,” World Science, vol. 7, pp. 1–5, 1983. View at Google Scholar
  15. S. D. Liu, L. B. Zhang, and Z. H. Wang, “Forecasting diagnosis of rolling bearing by GM model,” Lubrication Engineering, vol. 2, pp. 38-39, 2002. View at Google Scholar
  16. J. T Yang and W. L. Ye, “Application of grey model to machinery faults prediction,” Journal of Mechanical Strength, vol. 23, pp. 277–279, 2001. View at Google Scholar
  17. E. L. Liu, Research on rolling bearing life prediction method based on WPHM model [Ph.D. thesis], Dalian University of Technology, Dalian, China, 2014.
  18. M. Tabaszewski, “Prediction of diagnostic symptom values using a set of models GM(1,1) and a moving window method,” Diagnostyka, vol. 15, no. 3, pp. 65–68, 2014. View at Google Scholar · View at Scopus
  19. M. Tabaszewski and C. Cempel, “Using a set of GM(1,1) models to predict values of diagnostic symptoms,” Mechanical Systems and Signal Processing, vol. 52-53, no. 1, pp. 416–425, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. Z. H. Zhang, Theory and Engineering Applications of Reliability, Science Press, Beijing, China, 2012.
  21. Y. Li, Research and application of the grey forecast model [Ph.D. thesis], Zhejiang Sci-Tech University, Zhejiang, China, 2012.
  22. H. Qiu, J. Lee, J. Lin, and G. Yu, “Wavelet flter-based weak signature detection method and its application on rolling elementbearing prognostics,” Journal of Sound Vibration, vol. 289, pp. 1066–1090, 2006. View at Publisher · View at Google Scholar
  23. F. Wang, X. Chen, B. Dun, B. Wang, D. Yan, and H. Zhu, “Rolling bearing reliability assessment via kernel principal component analysis and weibull proportional hazard model,” Shock and Vibration, vol. 2017, Article ID 6184190, 11 pages, 2017. View at Publisher · View at Google Scholar · View at Scopus