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Journal of Sensors
Volume 2016, Article ID 9693651, 9 pages
http://dx.doi.org/10.1155/2016/9693651
Research Article

A Wavelet Based Multiscale Weighted Permutation Entropy Method for Sensor Fault Feature Extraction and Identification

College of Information Science & Technology, Beijing University of Chemical Technology, Beijing City Chaoyang District North Third Ring Road 15, Beijing 100029, China

Received 20 February 2016; Revised 5 May 2016; Accepted 8 May 2016

Academic Editor: Fanli Meng

Copyright © 2016 Qiaoning Yang and Jianlin Wang. 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. G. Heredia and A. Ollero, “Sensor fault detection in small autonomous helicopters using observer/Kalman filter identification,” in Proceedings of the IEEE International Conference on Mechatronics (ICM '09), pp. 1–6, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. J. C. D. Silva, A. Saxena, E. Balaban, and K. Goebel, “A knowledge-based system approach for sensor fault modeling, detection and mitigation,” Expert Systems with Applications, vol. 39, no. 12, pp. 10977–10989, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Zhang, J. Wu, and Y. Wang, “Sensor soft fault detection method of autonomous underwater vehicle,” in Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA '09), pp. 4839–4844, Changchun, China, August 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. H. Liu, M. Huang, I. Janghorban, P. Ghorbannezhad, and C. Yoo, “Faulty sensor detection, identification and reconstruction of indoor air quality measurements in a subway station,” in Proceedings of the 11th International Conference on Control, Automation and Systems (ICCAS '11), pp. 323–328, October 2011.
  5. W. He, Q. Miao, M. Azarian, and M. Pecht, “Health monitoring of cooling fan bearings based on wavelet filter,” Mechanical Systems and Signal Processing, vol. 64-65, pp. 149–161, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. R. Yan, Base wavelet selection criteria for non-stationary vibration analysis in bearing health diagnosis [Ph.D. thesis], University of Massachusetts Amherst, Amherst, Mass, USA, 2007.
  7. P. K. Kankar, S. C. Sharma, and S. P. Harsha, “Fault diagnosis of ball bearings using continuous wavelet transform,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 2300–2312, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Bandt and B. Pompe, “Permutation entropy: a natural complexity measure for time series,” Physical Review Letters, vol. 88, no. 17, Article ID 174102, 2002. View at Google Scholar · View at Scopus
  9. M. Zanin, L. Zunino, O. A. Rosso, and D. Papo, “Permutation entropy and its main biomedical and econophysics applications: a review,” Entropy, vol. 14, no. 8, pp. 1553–1577, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. W. Aziz and M. Arif, “Multiscale permutation entropy of physiological time series,” in Proceedings of the Pakistan Section Multitopic Conference, pp. 1–6, Karachi, Pakistan, December 2005. View at Publisher · View at Google Scholar
  11. S.-D. Wu, P.-H. Wu, C.-W. Wu, J.-J. Ding, and C.-C. Wang, “Bearing fault diagnosis based on multiscale permutation entropy and support vector machine,” Entropy, vol. 14, no. 8, pp. 1343–1356, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  12. P. L. Vuong, A. S. Malik, and J. Bornot, “Weighted-permutation entropy as complexity measure for electroencephalographic time series of different physiological states,” in Proceedings of the 3rd IEEE Conference on Biomedical Engineering and Sciences (IECBES '14), pp. 979–984, Kuala Lumpur, Malaysia, December 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. B. Fadlallah, B. Chen, A. Keil, and J. Principe, “Weighted-permutation entropy: a complexity measure for time series incorporating amplitude information,” Anesthesiology, vol. 87, no. 2, pp. 1647–1650, 2013. View at Google Scholar
  14. X. Chen, N.-D. Jin, A. Zhao, Z.-K. Gao, L.-S. Zhai, and B. Sun, “The experimental signals analysis for bubbly oil-in-water flow using multi-scale weighted-permutation entropy,” Physica A: Statistical Mechanics & its Applications, vol. 417, pp. 230–244, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Zhou, J. Xiao, H. Xiao, W. Zhang, W. Zhu, and C. Li, “Multifault diagnosis for rolling element bearings based on intrinsic mode permutation entropy and ensemble optimal extreme learning machine,” Advances in Mechanical Engineering, vol. 6, Article ID 803919, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. R. Yan, Y. Liu, and R. X. Gao, “Permutation entropy: a nonlinear statistical measure for status characterization of rotary machines,” Mechanical Systems and Signal Processing, vol. 29, pp. 474–484, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. X. Zhang, Y. Liang, J. Zhou, and Y. Zang, “A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM,” Measurement, vol. 69, pp. 164–179, 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Bian, C. Qin, Q. D. Y. Ma, and Q. Shen, “Modified permutation-entropy analysis of heartbeat dynamics,” Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, vol. 85, no. 2, Article ID 021906, pp. 439–441, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. U. Parlitz, S. Berg, S. Luther, A. Schirdewan, J. Kurths, and N. Wessel, “Classifying cardiac biosignals using ordinal pattern statistics and symbolic dynamics,” Computers in Biology and Medicine, vol. 42, no. 3, pp. 319–327, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. N. Nicolaou and J. Georgiou, “Detection of epileptic electroencephalogram based on permutation entropy and support vector Machines,” Expert Systems with Applications, vol. 39, no. 1, pp. 202–209, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Ravelo-García, J. Navarro-Mesa, U. Casanova-Blancas et al., “Application of the permutation entropy over the heart rate variability for the improvement of electrocardiogram-based sleep breathing pause detection,” Entropy, vol. 17, no. 3, pp. 914–927, 2015. View at Publisher · View at Google Scholar
  22. Y. Yin and P. Shang, “Weighted multiscale permutation entropy of financial time series,” Nonlinear Dynamics, vol. 78, no. 4, pp. 2921–2939, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. D. Cai, C. Zhang, and X. He, “Unsupervised feature selection for multi-cluster data,” in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '10), pp. 333–342, ACM, Washington, DC, USA, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Kang, M. R. Islam, J. Kim, J.-M. Kim, and M. Pecht, “A hybrid feature selection scheme for reducing diagnostic performance deterioration caused by outliers in data-driven diagnostics,” IEEE Transactions on Industrial Electronics, vol. 63, no. 5, pp. 3299–3310, 2016. View at Publisher · View at Google Scholar
  25. “Gas sensor arrays in open sampling settings Data set,” 2016, https://archive.ics.uci.edu/ml/datasets/Gas+sensor+arrays+in+open+sampling+settings.
  26. Q. Yang and J. Wang, “Multi-level wavelet shannon entropy-based method for single-sensor fault location,” Entropy, vol. 17, no. 10, pp. 7101–7117, 2015. View at Publisher · View at Google Scholar · View at Scopus
  27. J. Rodriguez, A. Perez, and J. Lozano, “Sensitivity analysis of k-fold cross validation in prediction error estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 3, pp. 569–575, 2010. View at Publisher · View at Google Scholar
  28. M. Kang, J. Kim, J.-M. Kim, A. C. C. Tan, E. Y. Kim, and B.-K. Choi, “Reliable fault diagnosis for low-speed bearings using individually trained support vector machines with kernel discriminative feature analysis,” IEEE Transactions on Power Electronics, vol. 30, no. 5, pp. 2786–2797, 2015. View at Publisher · View at Google Scholar · View at Scopus