Table of Contents Author Guidelines Submit a Manuscript
Shock and Vibration
Volume 2017, Article ID 7853918, 17 pages
https://doi.org/10.1155/2017/7853918
Research Article

Fault Detection of a Wheelset Bearing Based on Appropriately Sparse Impulse Extraction

State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China

Correspondence should be addressed to Jianming Ding; moc.621@gnimnaijgnidf

Received 16 November 2016; Revised 23 February 2017; Accepted 28 February 2017; Published 18 June 2017

Academic Editor: Mariano Artés

Copyright © 2017 Jianming Ding 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. H. C. Chose, Y. Wan, and A. K. Chan, “Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals: comparison of FFT, CWT, and DWT features,” in Proceedings of the SPIE-The International Society for Optical Engineering, pp. 480–496, 1997.
  2. J. Donelson III. and R. L. Dicus, “Bearing defect detection using on-board accelerometer measurements,” in Proceedings of the ASME/IEEE 2002 Joint Rail Conference, RTD 2002, pp. 95–102, Washington, usa, April 2002. View at Publisher · View at Google Scholar · View at Scopus
  3. 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
  4. H. Ocak, K. A. Loparo, and Discenzo F. M., “Online tracking of bearing wear using wavelet packet decomposition and probabilistic modelling—A method for bearing prognostics,” Journal of Sound and Vibration, vol. 302, pp. 951–961, 2007. View at Google Scholar
  5. Y. G. Lei, Z. J. He, and Y. Zi, “New clustering algorithm-based fault diagnosis using compensation distance evaluation techniquetechnique,” Mechanical Systems and Signal Processing, vol. 22, pp. 419–435, 2008. View at Google Scholar
  6. G. He, K. Ding, and H. Lin, “Fault feature extraction of rolling element bearings using sparse representation,” Journal of Sound and Vibration, vol. 366, pp. 514–527, 2016. View at Publisher · View at Google Scholar · View at Scopus
  7. R. Golafshan and K. Y. Sanliturk, “SVD and hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults,” Mechanical Systems and Signal Processing, vol. 70, no. 71, pp. 36–50, 2016. View at Google Scholar
  8. D. Wang, “An extension of the infograms to novel Bayesian inference for bearing fault feature identification,” Mechanical Systems and Signal Processing, vol. 80, pp. 19–30, 2016. View at Google Scholar
  9. D. Wang and K. Tsui, “Dynamic Bayesian wavelet transform: New methodology for extraction of repetitive transients,” Mechanical Systems and Signal Processing, vol. 88, pp. 137–144, 2017. View at Google Scholar
  10. H. Jiang, C. Li, and H. Li, “An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis,” Mechanical Systems and Signal Processing, vol. 36, no. 2, pp. 225–239, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. X. F. Chen, Z. H. Du, J. Li, X. Li, and H. Zhang, “Compressed sensing based on dictionary learning for extracting impulse components,” Signal Processing, vol. 96, pp. 94–109, 2014. View at Google Scholar
  12. R. Jiang, H. Qiao, and B. Zhang, “Efficient fisher discrimination dictionary learning,” Signal Processing, vol. 128, pp. 28–39, 2016. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311–4322, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. G. Davis, S. Mallat, and M. Avellaneda, “Adaptive greedy approximations,” Constructive Approximation, vol. 13, no. 1, pp. 57–98, 1997. View at Publisher · View at Google Scholar
  15. A. M. Tillmann, “On the computational intractability of exact and approximate dictionary learning,” IEEE Signal Processing Letters, vol. 22, pp. 45–49, 2015. View at Google Scholar
  16. Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, “Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition,” in Proceedings of the 27th Annu. Asilomar Conf. Signals, Systems, and Computers, Pacific Grove, CA, vol. 1, pp. 40–44, Pacific Grove, CA, 1993.
  17. J. A. Tropp, “Greed is good: algorithmic results for sparse approximation,” Institute of Electrical and Electronics Engineers. Transactions on Information Theory, vol. 50, no. 10, pp. 2231–2242, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  18. T. T. Cai and L. Wang, “Orthogonal matching pursuit for sparse signal recovery with noise,” IEEE Transactions on Information Theory, vol. 57, no. 7, pp. 4680–4688, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  19. A. Huang, G. Guan, and Q. Wan, “A block orthogonal matching pursuit algorithm based on sensing dictionary,” International Journal of Physical Sciences, vol. 6, pp. 992–999, 2011. View at Google Scholar · View at Scopus
  20. D. Needell and J. A. Troppb, “CoSaMP: iterative signal recovery from incomplete and inaccurate samples,” Applied and Computational Harmonic Analysis, vol. 26, pp. 301–321, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  21. W. Dai and O. Milenkovic, “Subspace pursuit for compressive sensing signal reconstruction,” IEEE Transactions on Information Theory, vol. 55, no. 5, pp. 2230–2249, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems,” IEEE Journal on Selected Topics in Signal Processing, vol. 1, no. 4, pp. 586–597, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. S. J. Wright, R. D. Nowak, and M. A. T. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE Transactions on Signal Processing, vol. 57, no. 7, pp. 2479–2493, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. I. Daubechies, M. Defrise, and C. de Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Communications on Pure and Applied Mathematics, pp. 1413–1457, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  25. A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM Journal on Imaging Sciences, vol. 2, pp. 183–202, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  26. S. Yun and K.-C. Toh, “A coordinate gradient descent method for l1-regularized convex minimization,” Computational Optimization and Applications, vol. 48, no. 2, pp. 273–307, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. W. Yin, S. Osher, D. Goldfarb, and J. Darbon, “Bregman iterative algorithms for l1-minimization with applications to compressed sensing,” SIAM Journal on Imaging Sciences, vol. 1, no. 1, pp. 143–168, 2008. View at Publisher · View at Google Scholar · View at MathSciNet
  28. D. L. Donoho, “For most large underdetermined systems of linear equations, the minimal l1-norm solution is also the sparsest solution,” Communications on Pure and Applied Mathematics, vol. 59, no. 7, pp. 907–934, 2006. View at Publisher · View at Google Scholar · View at MathSciNet
  29. S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Journal on Scientific Computing, vol. 20, no. 1, pp. 33–61, 1998. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  30. Z. Feng and F. Chu, “Application of atomic decomposition to gear damage detection,” Journal of Sound and Vibration, vol. 302, no. 1-2, pp. 138–151, 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. K. Skretting and K. Engan, “Recursive least squares dictionary learning algorithm,” IEEE Transactions on Signal Processing, vol. 58, no. 4, pp. 2121–2130, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  32. R. Rubinstein, T. Peleg, and M. Elad, “Analysis K-SVD: a dictionary-learning algorithm for the analysis sparse model,” IEEE Transactions on Signal Processing, vol. 61, no. 3, pp. 661–677, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  33. J. Mairal, F. Bach, and J. Ponce, “Online dictionary learning for sparse coding,” in Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09), pp. 689–696, 2009. View at Publisher · View at Google Scholar · View at Scopus
  34. K. Engan, K. Skretting, and J. H. Husøy, “Family of iterative LS-based dictionary learning algorithms, ILS-DLA, for sparse signal representation,” Digital Signal Processing: A Review Journal, vol. 17, no. 1, pp. 32–49, 2007. View at Publisher · View at Google Scholar · View at Scopus
  35. E. C. Smith and M. S. Lewicki, “Efficient auditory coding,” Nature, vol. 439, no. 7079, pp. 978–982, 2006. View at Publisher · View at Google Scholar · View at Scopus
  36. T. Blumensath and M. Davies, “Sparse and shift-invariant representations of music,” IEEE Transactions on Audio, Speech and Language Processing, vol. 14, no. 1, pp. 50–57, 2006. View at Publisher · View at Google Scholar · View at Scopus
  37. C. Rusu, B. Dumitrescu, and S. A. Tsaftaris, “Explicit shift-invariant dictionary learning,” IEEE Signal Processing Letters, vol. 21, no. 1, pp. 6–9, 2014. View at Publisher · View at Google Scholar · View at Scopus
  38. 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
  39. H. Yang, J. Mathew, and L. Ma, “Fault diagnosis of rolling element bearings using basis pursuit,” Mechanical Systems and Signal Processing, vol. 19, no. 2, pp. 341–356, 2005. View at Publisher · View at Google Scholar · View at Scopus
  40. Y. Qin, Y. Mao, and B. Tang, “Vibration signal component separation by iteratively using basis pursuit and its application in mechanical fault detection,” Journal of Sound and Vibration, vol. 332, no. 20, pp. 5217–5235, 2013. View at Publisher · View at Google Scholar · View at Scopus
  41. W. Fan, G. Cai, Z. K. Zhu, C. Shen, W. Huang, and L. Shang, “Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction,” Mechanical Systems and Signal Processing, vol. 56–57, pp. 230–245, 2015. View at Publisher · View at Google Scholar · View at Scopus
  42. Y. Wang, J. Xiang, Q. Mo, and S. He, “Compressed sparse time-frequency feature representation via compressive sensing and its applications in fault diagnosis,” Measurement, vol. 68, pp. 70–81, 2015. View at Publisher · View at Google Scholar · View at Scopus
  43. H. Tang, J. Chen, and G. Dong, “Sparse representation based latent components analysis for machinery weak fault detection,” Mechanical Systems and Signal Processing, vol. 46, no. 2, pp. 373–388, 2014. View at Publisher · View at Google Scholar · View at Scopus
  44. H. Liu, C. Liu, and Y. Huang, “Adaptive feature extraction using sparse coding for machinery fault diagnosis,” Mechanical Systems and Signal Processing, vol. 25, no. 2, pp. 558–574, 2011. View at Publisher · View at Google Scholar · View at Scopus
  45. H. Zhou, J. Chen, G. Dong, and R. Wang, “Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model,” Mechanical Systems and Signal Processing, vol. 72-73, pp. 65–79, 2016. View at Publisher · View at Google Scholar · View at Scopus
  46. Z. Feng and M. Liang, “Complex signal analysis for planetary gearbox fault diagnosis via shift invariant dictionary learning,” Measurement, vol. 90, pp. 382–395, 2016. View at Publisher · View at Google Scholar
  47. B. Wohlberg, “Efficient algorithms for convolutional sparse representations,” IEEE Transactions on Image Processing, vol. 25, no. 1, pp. 301–315, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  48. J. Antoni and R. B. Randall, “A stochastic model for simulation and diagnostics of rolling element bearings with localized faults,” Journal of Vibration and Acoustics, Transactions of the ASME, vol. 125, no. 3, pp. 282–289, 2003. View at Publisher · View at Google Scholar · View at Scopus
  49. P. W. Tse and D. Wang, “The design of a new sparsogram for fast bearing fault diagnosis: part 1 of the two related manuscripts that have a joint title as ‘Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement—parts 1 and 2’,” Mechanical Systems and Signal Processing, vol. 40, no. 2, pp. 520–544, 2013. View at Publisher · View at Google Scholar · View at Scopus
  50. R. Q. Yan, Y. B. Liu, and R. X. Gao, “Permutation entropy: a nonlinear statistical measure for status characterization of rotary machines,” Mechanical Systems and Signal Processing, vol. 29, no. 5, pp. 474–484, 2012. View at Publisher · View at Google Scholar · View at Scopus
  51. J. Antoni, “The infogram: Entropic evidence of the signature of repetitive transients,” Mechanical Systems and Signal Processing, vol. 74, pp. 73–94, 2016. View at Publisher · View at Google Scholar · View at Scopus
  52. L. Zhang, G. Xiong, and W. Huang, “New procedure and index for the parameter optimization of complex wavelet based resonance demodulation,” vol. 51, no. 3, pp. 129–138, 2015. View at Publisher · View at Google Scholar · View at Scopus