Table of Contents Author Guidelines Submit a Manuscript
Shock and Vibration
Volume 2018, Article ID 9067184, 15 pages
https://doi.org/10.1155/2018/9067184
Research Article

An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation Prognostics

1School of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China
2Sichuan Special Equipment Inspection and Research Institute, Chengdu 610051, China

Correspondence should be addressed to Lixiang Duan; nc.ude.puc@xlnaud

Received 4 August 2017; Revised 21 December 2017; Accepted 15 January 2018; Published 18 February 2018

Academic Editor: Rafał Burdzik

Copyright © 2018 Lixiang Duan 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. M. Ge, Y. Xu, and R. Du, “An intelligent online monitoring and diagnostic system for manufacturing automation,” IEEE Transactions on Automation Science and Engineering, vol. 5, no. 1, pp. 127–138, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. X. An, H. Zeng, and C. Li, “Demodulation analysis based on adaptive local iterative filtering for bearing fault diagnosis,” Measurement, vol. 94, no. 12, pp. 554–560, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. K. Peng and F. L. Chu, “Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography,” Mechanical Systems and Signal Processing, vol. 18, no. 2, pp. 199–221, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. K. Javed, R. Gouriveau, N. Zerhouni, and P. Nectoux, “A feature extraction procedure based on trigonometric functions and cumulative descriptors to enhance prognostics modeling,” in Proceedings of the 2013 IEEE International Conference on Prognostics and Health Management, PHM 2013, pp. 1–7, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Dong and T. Luo, “Bearing degradation process prediction based on the PCA and optimized LS-SVM model,” Measurement, vol. 46, no. 9, pp. 3143–3152, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. X. H. Liang, M. J. Zuo, and M. R. Hoseini, “Vibration signal modeling of a planetary gear set for tooth crack detection,” Engineering Failure Analysis, vol. 48, pp. 185–200, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Lee, F. Wu, W. Zhao, M. Ghaffari, L. Liao, and D. Siegel, “Prognostics and health management design for rotary machinery systems—reviews, methodology and applications,” Mechanical Systems and Signal Processing, vol. 42, no. 1-2, pp. 314–334, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Shen, D. Wang, F. Kong, and P. W. Tse, “Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier,” Measurement, vol. 46, no. 4, pp. 1551–1564, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. A. K. S. Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1483–1510, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Javed, R. Gouriveau, N. Zerhouni, and P. Nectoux, “Enabling health monitoring approach based on vibration data for accurate prognostics,” IEEE Transactions on Industrial Electronics, vol. 62, no. 1, pp. 647–656, 2015. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Kumar and M. Pecht, “Modeling approaches for prognostics and health management of electronics,” International Journal of Performability Engineering, vol. 6, no. 5, pp. 222–229, 2010. View at Google Scholar
  12. F. Camci, K. Medjaher, N. Zerhouni, and P. Nectoux, “Feature evaluation for effective bearing prognostics,” Quality and Reliability Engineering International, vol. 29, no. 4, pp. 477–486, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. B. Zhang, L. Zhang, and J. Xu, “Degradation feature selection for remaining useful life prediction of rolling element bearings,” Quality and Reliability Engineering International, vol. 32, no. 2, pp. 547–554, 2016. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Yan, C. Guo, and X. Wang, “A dynamic multi-scale Markov model based methodology for remaining life prediction,” Mechanical Systems and Signal Processing, vol. 25, no. 4, pp. 1364–1376, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. P. Li, F. Kong, Q. He, and Y. Liu, “Multiscale slope feature extraction for rotating machinery fault diagnosis using wavelet analysis,” Measurement, vol. 46, no. 1, pp. 497–505, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Hong and M. Liang, “Fault severity assessment for rolling element bearings using the Lempel-Ziv complexity and continuous wavelet transform,” Journal of Sound and Vibration, vol. 320, no. 1-2, pp. 452–468, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. W. Guo, P. W. Tse, and A. Djordjevich, “Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition,” Measurement, vol. 45, no. 5, pp. 1308–1322, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. E. Sutrisno, H. Oh, A. S. S. Vasan, and M. Pecht, “Estimation of remaining useful life of ball bearings using data driven methodologies,” in Proceedings of the IEEE Conference on Prognostics and Health Management (PHM '12), pp. 1–7, Denver, Colo, USA, June 2012. View at Publisher · View at Google Scholar
  19. T. H. Loutas, D. Roulias, and G. Georgoulas, “Remaining useful life estimation in rolling bearings utilizing data-driven probabilistic E-support vectors regression,” IEEE Transactions on Reliability, vol. 62, no. 4, pp. 821–832, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Ben Ali, B. Chebel-Morello, L. Saidi, S. Malinowski, and F. Fnaiech, “Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network,” Mechanical Systems and Signal Processing, vol. 56, pp. 150–172, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. H. Qiu, J. Lee, J. Lin, and G. Yu, “Robust performance degradation assessment methods for enhanced rolling element bearing prognostics,” Advanced Engineering Informatics, vol. 17, no. 3-4, pp. 127–140, 2003. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Sassi, B. Badri, and M. Thomas, “Tracking surface degradation of ball bearings by means of new time domain scalar indicators,” International Journal of COMADEM, vol. 11, no. 3, pp. 36–45, 2008. View at Google Scholar · View at Scopus
  23. S. Zhang, Y. Zhang, and D. Zhu, “Residual Life Prediction for Rolling Element Bearings Based on an Effective Degradation Indicator,” Journal of Failure Analysis and Prevention (JFAP), vol. 15, no. 5, pp. 722–729, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. Z. Xi, R. Jing, P. Wang, and C. Hu, “A copula-based sampling method for data-driven prognostics,” Reliability Engineering & System Safety, vol. 132, no. 4, pp. 72–82, 2014. View at Google Scholar
  25. L. Liao, “Discovering prognostic features using genetic programming in remaining useful life prediction,” IEEE Transactions on Industrial Electronics, vol. 61, no. 5, pp. 2464–2472, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. C. Sun, Z. He, H. Cao, and Z. Zhang, “A non-probabilistic metric derived from condition information for operational reliability assessment of aero-engines,” IEEE Transactions on Reliability, vol. 64, no. 1, pp. 1–15, 2014. View at Google Scholar
  27. Y. Wang and H. Pham, “Imperfect preventive maintenance policies for two-process cumulative damage model of degradation and random shocks,” International Journal of Systems Assurance Engineering and Management, vol. 2, no. 1, pp. 66–77, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. X. Ni, J. Zhao, X. Zhang, X. Lv, and J. Zhao, “System degradation process modeling for two-stage degraded mode,” in Proceedings of the 2014 Prognostics and System Health Management Conference, PHM 2014, pp. 186–189, August 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Ragab, S. Yacout, M.-S. Ouali, and H. Osman, “Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions,” Journal of Intelligent Manufacturing, pp. 1–20, 2016. View at Publisher · View at Google Scholar · View at Scopus
  30. M. X. Zhu, J. N. Zhang, Y. Li, and Y. H. Wei, “Partial discharge signals separation using cumulative energy function and mathematical morphology gradient,” IEEE Transactions on Dielectrics Electrical Insulation, vol. 23, no. 1, pp. 482–493, 2016. View at Publisher · View at Google Scholar
  31. S. Porotsky and Z. Bluvband, “Remaining useful life estimation for systems with non-trendability behaviour,” in Proceedings of the 2012 IEEE International Conference on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHM Technology and Application, PHM 2012, pp. 1–6, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. J. B. Coble, Merging data sources to predict remaining useful life—an automated method to identify prognostic parameters, [Ph.D. thesis], University of Tennessee, 2010.
  33. Q. He, F. Kong, and R. Yan, “Subspace-based gearbox condition monitoring by kernel principal component analysis,” Mechanical Systems and Signal Processing, vol. 21, no. 4, pp. 1755–1772, 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. F. Takens, “Detecting strange attractors in turbulence,” in Dynamical systems and Turbulence, D. A. Rand and L. S. Young, Eds., vol. 898 of Lecture Note in Mathematics, pp. 366–381, Springer, Berlin, Germany, 1981. View at Publisher · View at Google Scholar · View at MathSciNet
  35. A. Bellini, F. Filippetti, C. Tassoni, and G.-A. Capolino, “Advances in diagnostic techniques for induction machines,” IEEE Transactions on Industrial Electronics, vol. 55, no. 12, pp. 4109–4126, 2008. View at Publisher · View at Google Scholar · View at Scopus
  36. L. Liao and J. Lee, “A novel method for machine performance degradation assessment based on fixed cycle features test,” Journal of Sound and Vibration, vol. 326, no. 3-5, pp. 894–908, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  38. http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository.