Table of Contents Author Guidelines Submit a Manuscript
Shock and Vibration
Volume 2016, Article ID 1485412, 13 pages
http://dx.doi.org/10.1155/2016/1485412
Research Article

Adaptive Multiscale Noise Control Enhanced Stochastic Resonance Method Based on Modified EEMD with Its Application in Bearing Fault Diagnosis

1College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2College of Liren, Yanshan University, Qinhuangdao 066004, China

Received 30 June 2016; Accepted 28 September 2016

Academic Editor: Ganging Song

Copyright © 2016 Jimeng Li and Jinfeng Zhang. 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. X. W. Zhang, R. X. Gao, R. Q. Yan, X. F. Chen, C. Sun, and Z. B. Yang, “Multivariable wavelet finite element-based vibration model for quantitative crack identification by using particle swarm optimization,” Journal of Sound and Vibration, vol. 375, pp. 200–216, 2016. View at Publisher · View at Google Scholar
  2. R. N. Liu, B. Y. Yang, X. L. Zhang, S. B. Wang, and X. F. Chen, “Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis,” Mechanical Systems and Signal Processing, vol. 75, no. 15, pp. 345–370, 2016. View at Publisher · View at Google Scholar
  3. Y. K. Akilu, J. K. Sinha, and K. Elbhbah, “An improved data fusion technique for faults diagnosis in rotating machines,” Measurement, vol. 58, pp. 27–32, 2015. View at Google Scholar
  4. Y. Wang, R. Markert, J. Xiang, and W. Zheng, “Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system,” Mechanical Systems and Signal Processing, vol. 60, pp. 243–251, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. K. Dragomiretskiy and D. Zosso, “Variational mode decomposition,” IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531–544, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. S. Wang, X. Chen, G. Li, X. Li, and Z. He, “Matching demodulation transform with application to feature extraction of rotor rub-impact fault,” IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 5, pp. 1372–1383, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. C. Li and M. Liang, “A generalized synchrosqueezing transform for enhancing signal time-frequency representation,” Signal Processing, vol. 92, no. 9, pp. 2264–2274, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Cai, X. Chen, and Z. He, “Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox,” Mechanical Systems and Signal Processing, vol. 41, no. 1-2, pp. 34–53, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Yuan, C. J. Wei, B. Zou et al., “A comparative study on multiwavelet construction methods and customized multiwavelet library for mechanical fault detection,” Shock and Vibration, vol. 2015, Article ID 963528, 12 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. H. R. Cao, F. Fan, K. Zhou, and Z. J. He, “Wheel-bearing fault diagnosis of trains using empirical wavelet transform,” Measurement, vol. 82, pp. 439–449, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. Lei, D. Han, J. Lin, and Z. He, “Planetary gearbox fault diagnosis using an adaptive stochastic resonance method,” Mechanical Systems and Signal Processing, vol. 38, no. 1, pp. 113–124, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Benzi, A. Sutera, and A. Vulpiani, “The mechanism of stochastic resonance,” Journal of Physics A: Mathematical and General, vol. 14, no. 11, pp. L453–L457, 1981. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. C. Nicolis and G. Nicolis, “Stochastic aspects of climatic transitions—additive fluctuations,” Tellus, vol. 33, no. 3, pp. 225–234, 1981. View at Google Scholar · View at MathSciNet
  14. R. Zhao, R. Q. Yan, and R. X. Gao, “Dual-scale cascaded adaptive stochastic resonance for rotary machine health monitoring,” Journal of Manufacturing Systems, vol. 32, no. 4, pp. 529–535, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. X. F. Zhang, N. Q. Hu, L. Hu, and Z. Cheng, “Multi-scale bistable stochastic resonance array: a novel weak signal detection method and application in machine fault diagnosis,” Science China Technological Sciences, vol. 56, no. 9, pp. 2115–2123, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Qin, Y. Tao, Y. He, and B. P. Tang, “Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction,” Journal of Sound and Vibration, vol. 333, no. 26, pp. 7386–7400, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. D. Y. Han, P. Li, S. J. An, and P. M. Shi, “Multi-frequency weak signal detection based on wavelet transform and parameter compensation band-pass multi-stable stochastic resonance,” Mechanical Systems and Signal Processing, vol. 70-71, pp. 995–1010, 2016. View at Publisher · View at Google Scholar
  18. J. Wang, Q. B. He, and F. R. Kong, “Adaptive multiscale noise tuning stochastic resonance for health diagnosis of rolling element bearings,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 2, pp. 564–577, 2015. View at Publisher · View at Google Scholar · View at Scopus
  19. X.-H. Chen, G. Cheng, X.-L. Shan, X. Hu, Q. Guo, and H.-G. Liu, “Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance,” Measurement, vol. 73, pp. 55–67, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. Z.-H. Lai and Y.-G. Leng, “Generalized parameter-adjusted stochastic resonance of duffing oscillator and its application to weak-signal detection,” Sensors, vol. 15, no. 9, pp. 21327–21349, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Li, X. Chen, Z. Du, Z. Fang, and Z. He, “A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis,” Renewable Energy, vol. 60, pp. 7–19, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. F. B. Duan, F. Chapeau-Blondeau, and D. Abbott, “Double-maximum enhancement of signal-to-noise ratio gain via stochastic resonance and vibrational resonance,” Physical Review E, vol. 90, no. 2, Article ID 022134, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. View at Publisher · View at Google Scholar
  24. Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis, vol. 1, no. 1, pp. 1–41, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Lei, Z. He, and Y. Zi, “Application of the EEMD method to rotor fault diagnosis of rotating machinery,” Mechanical Systems and Signal Processing, vol. 23, no. 4, pp. 1327–1338, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. Q. He, J. Wang, Y. Liu, D. Dai, and F. Kong, “Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines,” Mechanical Systems and Signal Processing, vol. 28, pp. 443–457, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. B. Hu and B. Li, “A new multiscale noise tuning stochastic resonance for enhanced fault diagnosis in wind turbine drivetrains,” Measurement Science and Technology, vol. 27, no. 2, Article ID 025017, 2016. View at Publisher · View at Google Scholar · View at Scopus
  28. J. J. Zhang and T. Zhang, “Parameter-induced stochastic resonance based on spectral entropy and its application to weak signal detection,” Review of Scientific Instruments, vol. 86, no. 2, Article ID 025005, pp. 1–6, 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. http://csegroups.case.edu/bearingdatacenter/home
  30. Y. X. Wang, J. W. Xiang, R. Markert, and M. Liang, “Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: a review with applications,” Mechanical Systems and Signal Processing, vol. 66-67, pp. 679–698, 2016. View at Publisher · View at Google Scholar · View at Scopus