A Novel Method for Adaptive Multiresonance Bands Detection Based on VMD and Using MTEO to Enhance Rolling Element Bearing Fault Diagnosis

Vibration signals of the defect rolling element bearings are usually immersed in strong background noise, which make it difficult to detect the incipient bearing defect. In our paper, the adaptive detection of the multiresonance bands in vibration signal is firstly considered based on variational mode decomposition (VMD). As a consequence, the novel method for enhancing rolling element bearing fault diagnosis is proposed. Specifically, the method is conducted by the following three steps. First, the VMD is introduced to decompose the raw vibration signal. Second, the one or more modes with the information of fault-related impulses are selected through the kurtosis index. Third, Multiresolution Teager Energy Operator (MTEO) is employed to extract the fault-related impulses hidden in the vibration signal and avoid the negative value phenomenon of Teager Energy Operator (TEO). Meanwhile, the physical meaning of MTEO is also discovered in this paper. In addition, an idea of combining the multiresonance bands is constructed to further enhance the fault-related impulses. The simulation studies and experimental verifications confirm that the proposed method is effective for identifying the multiresonance bands and enhancing rolling element bearing fault diagnosis by comparing with Hilbert transform, EMD-based demodulation, and fast Kurtogram analysis.

[1]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[2]  M. Hestenes.  Multiplier and gradient methods , 1969 .

[3]  R. Tyrrell Rockafellar,et al.  A dual approach to solving nonlinear programming problems by unconstrained optimization , 1973, Math. Program..

[4]  Yang Yu,et al.  The application of energy operator demodulation approach based on EMD in machinery fault diagnosis , 2007 .

[5]  Petros Maragos,et al.  On amplitude and frequency demodulation using energy operators , 1993, IEEE Trans. Signal Process..

[6]  Lei Guo,et al.  Rolling Bearing Fault Classification Based on Envelope Spectrum and Support Vector Machine , 2009 .

[7]  Petros Maragos,et al.  Energy separation in signal modulations with application to speech analysis , 1993, IEEE Trans. Signal Process..

[8]  Iqbal Gondal,et al.  Inchoate Fault Detection Framework: Adaptive Selection of Wavelet Nodes and Cumulant Orders , 2012, IEEE Transactions on Instrumentation and Measurement.

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

[11]  Miguel A. Ferrer,et al.  Application of the Teager-Kaiser energy operator in bearing fault diagnosis. , 2013, ISA transactions.

[12]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..

[13]  Dejie Yu,et al.  Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings , 2005 .

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

[15]  A Santhana Raj,et al.  A novel application of Lucy–Richardson deconvolution: bearing fault diagnosis , 2015 .

[16]  Han Zhang,et al.  Compressed sensing based on dictionary learning for extracting impulse components , 2014, Signal Process..

[17]  Kota Solomon Raju,et al.  Comparative study between VMD and EMD in bearing fault diagnosis , 2014, 2014 9th International Conference on Industrial and Information Systems (ICIIS).

[18]  I. Soltani Bozchalooi,et al.  An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection ☆ , 2010 .

[19]  Jong-Ho Choi,et al.  Neural action potential detector using multi-resolution TEO , 2002 .

[20]  Jin Jiang,et al.  Time-frequency feature representation using energy concentration: An overview of recent advances , 2009, Digit. Signal Process..

[21]  Taejeong Kim,et al.  A new action potential detector using the MTEO and its effects on spike sorting systems at low signal-to-noise ratios , 2006, IEEE Transactions on Biomedical Engineering.

[22]  Petros Maragos,et al.  A comparison of the energy operator and the Hilbert transform approach to signal and speech demodulation , 1994, Signal Process..

[23]  Christos Yiakopoulos,et al.  Wavelet Based Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings , 2002 .

[24]  Yanxue Wang,et al.  Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system , 2015 .

[25]  Yanyang Zi,et al.  A Comparative Study on the Local Mean Decomposition and Empirical Mode Decomposition and Their Applications to Rotating Machinery Health Diagnosis , 2010 .

[26]  F. Hlawatsch,et al.  Linear and quadratic time-frequency signal representations , 1992, IEEE Signal Processing Magazine.

[27]  Jing Wang,et al.  Basic pursuit of an adaptive impulse dictionary for bearing fault diagnosis , 2014, 2014 International Conference on Mechatronics and Control (ICMC).

[28]  Robert B. Randall,et al.  On the use of the cyclic power spectrum in rolling element bearings diagnostics , 2005 .

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

[30]  Dimitri P. Bertsekas,et al.  Constrained Optimization and Lagrange Multiplier Methods , 1982 .

[31]  Alberto Bellini,et al.  Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals , 2009, IEEE Transactions on Industrial Electronics.

[32]  Jianfeng Zheng,et al.  Rolling bearings fault diagnosis based on energy operator demodulation approach , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[33]  J. Antoni.  Cyclic spectral analysis of rolling-element bearing signals : Facts and fictions , 2007 .

[34]  Gaigai Cai,et al.  Transient signal analysis based on Levenberg–Marquardt method for fault feature extraction of rotating machines , 2015 .

[35]  M. Feldman.  Hilbert transform in vibration analysis , 2011 .

[36]  Qingbo He,et al.  Time–frequency manifold correlation matching for periodic fault identification in rotating machines , 2013 .

[37]  Jin Chen,et al.  Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform , 2014 .

[38]  Ranjit Thuraisingham.  Estimation of Teager energy using the Hilbert-Huang transform , 2015, IET Signal Process..

[39]  L. S. Qu,et al.  Defect Detection for Bearings Using Envelope Spectra of Wavelet Transform , 2004 .

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

[41]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[42]  Fanrang Kong,et al.  An improved multiscale noise tuning of stochastic resonance for identifying multiple transient faults in rolling element bearings , 2014 .

[43]  Chao Jiang,et al.  A method of fault diagnosis for rolling element bearings based on non-parametric atom matching , 2014 .