Making EEMD more effective in extracting bearing fault features for intelligent bearing fault diagnosis by using blind fault component separation

[1]  Àngela Nebot,et al.  Fault diagnosis system based on fuzzy logic: Application to a valve actuator benchmark , 2011, J. Intell. Fuzzy Syst..

[2]  Changqing Shen,et al.  A parameterized Doppler distorted matching model for periodic fault identification in locomotive bearing , 2016 .

[3]  Chuan Li,et al.  Continuous-scale mathematical morphology-based optimal scale band demodulation of impulsive feature for bearing defect diagnosis , 2012 .

[4]  Peter W. Tse,et al.  An enhanced empirical mode decomposition method for blind component separation of a single-channel vibration signal mixture , 2016 .

[5]  Hasmat Malik,et al.  EMD and ANN based intelligent fault diagnosis model for transmission line , 2017, J. Intell. Fuzzy Syst..

[6]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[7]  Fanrang Kong,et al.  Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier , 2013 .

[8]  Yaguo Lei,et al.  EEMD method and WNN for fault diagnosis of locomotive roller bearings , 2011, Expert Syst. Appl..

[9]  Robert X. Gao,et al.  Performance enhancement of ensemble empirical mode decomposition , 2010 .

[10]  Yaguo Lei,et al.  Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition , 2013, Sensors.

[11]  Leiting Chen,et al.  Building cognizance rule knowledge for fault diagnosis based on fuzzy rough sets , 2015, J. Intell. Fuzzy Syst..

[12]  Yang Yu,et al.  A fault diagnosis approach for roller bearings based on EMD method and AR model , 2006 .

[13]  P. Tse,et al.  An improved Hilbert–Huang transform and its application in vibration signal analysis , 2005 .

[14]  P. Tse,et al.  A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing , 2005 .

[15]  J. Antoni Blind separation of vibration components: Principles and demonstrations , 2005 .

[16]  Ming Liang,et al.  Separation of fault features from a single-channel mechanical signal mixture using wavelet decomposition , 2007 .

[17]  Yu Yang,et al.  A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM , 2007 .

[18]  Wei Liang,et al.  Health Assessment of Cooling Fan Bearings Using Wavelet-Based Filtering , 2012, Sensors.

[19]  S.M. Kay,et al.  Spectrum analysis—A modern perspective , 1981, Proceedings of the IEEE.

[20]  Jianhui Lin,et al.  Faults Diagnostics of Railway Axle Bearings Based on IMF’s Confidence Index Algorithm for Ensemble EMD , 2015, Sensors.

[21]  Chuan Li,et al.  Multi-scale autocorrelation via morphological wavelet slices for rolling element bearing fault diagnosis , 2012 .

[22]  Robert X. Gao,et al.  Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring , 2006, IEEE Transactions on Instrumentation and Measurement.

[23]  Changqing Shen,et al.  A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis , 2013 .

[24]  Fanrang Kong,et al.  Machine fault signature analysis by midpoint-based empirical mode decomposition , 2011 .

[25]  Dong Wang,et al.  A new blind fault component separation algorithm for a single-channel mechanical signal mixture , 2012 .

[26]  Fulei Chu,et al.  Ensemble Empirical Mode Decomposition-Based Teager Energy Spectrum for Bearing Fault Diagnosis , 2013 .

[27]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[28]  Peter W. Tse,et al.  A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals , 2013 .

[29]  Peter W. Tse,et al.  An enhanced Kurtogram method for fault diagnosis of rolling element bearings , 2013 .

[30]  Zhipeng Feng,et al.  Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation , 2012 .

[31]  Yaguo Lei,et al.  Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs , 2009 .

[32]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[33]  Chuan Li,et al.  Criterion fusion for spectral segmentation and its application to optimal demodulation of bearing vibration signals , 2015 .

[34]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[35]  Peter W. Tse,et al.  Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition , 2012 .