Rolling bearing fault diagnosis based on partially ensemble empirical mode decomposition and variable predictive model-based class discrimination

This publication has 32 references indexed in Scilit:

• Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples
  Mechanical Systems and Signal Processing, 2013
• A kurtosis-guided adaptive demodulation technique for bearing fault detection based on tunable-Q wavelet transform
  Measurement Science and Technology, 2013
• An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis
  Mechanical Systems and Signal Processing, 2013
• Multi-scale autocorrelation via morphological wavelet slices for rolling element bearing fault diagnosis
  Mechanical Systems and Signal Processing, 2012
• Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines
  Mechanical Systems and Signal Processing, 2012
• Time–frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform
  Mechanical Systems and Signal Processing, 2012
• A roller bearing fault diagnosis method based on EMD energy entropy and ANN
  Journal of Sound and Vibration, 2006
• Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings
  Mechanical Systems and Signal Processing, 2005
• A confidence limit for the empirical mode decomposition and Hilbert spectral analysis
  Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2003
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
  Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1998