Fault identification and severity assessment of rolling element bearings based on EMD and fast kurtogram

Faults in rolling element bearings often cause the breakdown of rotating machinery. Not only the fault type identification but also the fault severity assessment is important. So this paper emphasizes the fault severity assessment. The method proposed in this paper contains two steps: first, identify the fault type based on the combination of empirical mode decomposition (EMD) and fast kurtogram; Second, assess the fault severity. In the first step, the original signal is firstly decomposed into some intrinsic mode functions (IMFs) and the representative IMFs are selected based on correlation analysis, and then the reconstruction signal (RS) is generated; Secondly, the fast kurtogram method is applied to the RS, and the optimum band width and center frequency is obtained. The fault type can be identified based on the fault characteristic frequency marked in the envelope demodulation spectrum. In the second step, the energy percentage of the most fault-related IMF is chosen as an indicator of the fault severity assessment. Experimental data of rolling element bearings inner raceway fault (IRF) with three severities at four running speeds were analyzed. The results show that the IRF identification and fault severity assessment is realized. The breakthrough attempt provides the great potential in the application of condition monitoring of bearings.

[1]  Jérôme Antoni.  The spectral kurtosis of nonstationary signals: Formalisation, some properties, and application , 2004, 2004 12th European Signal Processing Conference.

[2]  J. Antoni.  The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .

[3]  Yi Yang,et al.  A rotating machinery fault diagnosis method based on local mean decomposition , 2012, Digit. Signal Process..

[4]  H Engja,et al.  VIBRATION ANALYSIS USED FOR DETECTION OF ROLLER BEARING FAILURES , 1977 .

[5]  P. Tse,et al.  A Doppler Transient Model Based on the Laplace Wavelet and Spectrum Correlation Assessment for Locomotive Bearing Fault Diagnosis , 2013, Sensors.

[6]  Jiawei Xiang,et al.  Rolling element bearing fault detection using PPCA and spectral kurtosis , 2015 .

[7]  Min-Chun Pan,et al.  An insight concept to select appropriate IMFs for envelope analysis of bearing fault diagnosis , 2012 .

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

[9]  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.

[10]  Kang Zhang,et al.  An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems , 2012 .

[11]  Yu Yang,et al.  Local rub-impact fault diagnosis of the rotor systems based on EMD , 2009 .

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

[13]  Xiaofeng Liu,et al.  Application of correlation matching for automatic bearing fault diagnosis , 2012 .

[14]  Wei Li,et al.  Identification and diagnosis of concurrent faults in rotor-bearing system with WPT and zero space classifiers , 2014 .

[15]  V. Rai,et al.  Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform , 2007 .

[16]  Yang Yu,et al.  A roller bearing fault diagnosis method based on EMD energy entropy and ANN , 2006 .

[17]  Dejie Yu,et al.  A gear fault diagnosis using Hilbert spectrum based on MODWPT and a comparison with EMD approach , 2009 .

[18]  Rajesh Kumar,et al.  Thrust bearing groove race defect measurement by wavelet decomposition of pre-processed vibration signal , 2013 .

[19]  Robert B. Randall,et al.  Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram , 2009 .

[20]  N. Tandon,et al.  Defect detection in deep groove ball bearing in presence of external vibration using envelope analysis and Duffing oscillator , 2012 .

[21]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[22]  Yaguo Lei,et al.  Application of the EEMD method to rotor fault diagnosis of rotating machinery , 2009 .

[23]  Xiaofeng Liu,et al.  Bearing faults diagnostics based on hybrid LS-SVM and EMD method , 2015 .

[24]  J. Antoni.  Fast computation of the kurtogram for the detection of transient faults , 2007 .

[25]  Jing Yuan,et al.  Multiwavelet transform and its applications in mechanical fault diagnosis – A review , 2014 .

[26]  Cong Wang,et al.  Non-negative EMD manifold for feature extraction in machinery fault diagnosis , 2015 .

[27]  Jianhua Zhao,et al.  Probabilistic Principal Component Analysis for 2D data , 2011 .