Open Access
Issue
ITM Web Conf.
Volume 11, 2017
2017 International Conference on Information Science and Technology (IST 2017)
Article Number 08002
Number of page(s) 9
Section Session VIII: Signal Processing
DOI https://doi.org/10.1051/itmconf/20171108002
Published online 23 May 2017
  1. Y.G. Lei, D.T. Kong, J. Lin, M.J. Zuo, Fault detection of planetary gearboxes using new diagnostic parameters, Meas. Sci. Technol., 23 (2012) 055605–055615. [CrossRef] [Google Scholar]
  2. K.E. Ko, D.H. Lim, P.Y. Kim, J. Park, A study on the bending stress of the hollow sun gear in a planetary gear train, J. Mech. Sci. Technol., 24 (2010) 29–32. [CrossRef] [Google Scholar]
  3. N.E. Huang, Z. Shen, S.R. Long, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. London., 454 (1998) 903–995. [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  4. X.M. Xue, J.Z. Zhou, Y.H. Xu, W.L. Zhu, C.S. Li, An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis, Mech. Syst. Signal Proc., 62-63 (2015) 444–459. [CrossRef] [Google Scholar]
  5. J.R. Yeh, J.S. Shieh, N.E. Huang, Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method, Adv. Adapt. Data Anal., 2(2) (2010) 135–156. [CrossRef] [MathSciNet] [Google Scholar]
  6. L. Zhang, G. Xiong, H. Liu, Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference, Expert Syst. Appl., 37 (2010) 6077–6085. [CrossRef] [Google Scholar]
  7. W. Guo, L.J. Huang, C. Chen, H.W. Zou, Z.W. Liu, Elimination of end effects in local mean decomposition using spectral coherence and applications for rotating machinery, Digit. Signal Prog., 55 (2016) 52–63. [CrossRef] [Google Scholar]
  8. M. Akhoondzadeh, A MLP neural network as an investigator of TEC time series to detect seismo-ionospheric anomalies, Adv. Space. Res., 51 (2013) 2048–2057. [CrossRef] [Google Scholar]
  9. X.H. Chen, G. Cheng, X.L. Shan, X. Hu, Q. Guo, H.G. Liu, Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance, Measurement, 73 (2015) 55–67. [CrossRef] [Google Scholar]
  10. M.A. Colominasa, G. Schlotthauera, M.E. Torresa, Improved complete ensemble EMD: A suitable tool for biomedical signal processing. Biomed. Signal Process. Control, 14 (2014) 19–29. [CrossRef] [Google Scholar]
  11. D.R. Kong, H.B. Xie, Use of modified sample entropy measurement to classify ventricular tachycardia and fibrillation, Measurement, 44(4) (2011) 653–662. [CrossRef] [Google Scholar]
  12. J.D. Zheng, J.S. Cheng, Y. Yang, S.R. Luo, A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination, Mech. Mach. Theory, 78(16) (2014) 187–200. [CrossRef] [Google Scholar]
  13. S. Souahlia, K. Bacha, A. Chaari, MLP neural network-based decision for power transformers fault diagnosis using an improved combination of rogers and doernenburg ratios DGA, Electr. Power Energy Syst., 43 (2012) 1346–1353. [CrossRef] [Google Scholar]
  14. V.N. Ghate, S.V. Dudul, Optimal MLP neural network classifier for fault detection of three phase induction motor, Expert Syst. Appl., 37 (2010) 3468–3481. [CrossRef] [Google Scholar]

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