Periodicity-based kurtogram for random impulse resistance

The kurtogram developed from spectral kurtosis has been proven as an efficient tool for extracting the fault impulses in the diagnosis of rolling element bearings and gearboxes. Although the optimal narrowband chosen for demodulation by kurtogram is accurate and effective in experimental environment, this approach is very sensitive to large random impulses that are frequently encountered in industrial applications. The narrowband with maximum kurtosis is always associated with large interferential impulses, rather than the bearing fault. To overcome this limitation, the periodic component to aperiodic component ratio (PAR) is utilized in this article to differentiate the two types of impulses. The novel method named the PAR-based kurtogram focuses on finding the significant frequency band with periodic impulses. The effectiveness of the proposed method is verified by simulations, a test rig of locomotive rolling element bearings, and bearing data from the Case Western Reserve University. The results show that the PAR-based kurtogram improves the robustness to interference from aperiodic impulses significantly, which is very useful for bearing faults diagnosis.

[1]  Ariel Salomon,et al.  Use of temporal information: detection of periodicity, aperiodicity, and pitch in speech , 2005, IEEE Transactions on Speech and Audio Processing.

[2]  Tomasz Barszcz,et al.  A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram , 2011 .

[3]  Umberto Meneghetti,et al.  Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings , 2001 .

[4]  Yaguo Lei,et al.  Application of an improved kurtogram method for fault diagnosis of rolling element bearings , 2011 .

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

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

[7]  Jérôme Antoni,et al.  Detection of signal component modulations using modulation intensity distribution , 2012 .

[8]  Robert B. Randall,et al.  The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis , 2007 .

[9]  James F. Glockner,et al.  Case 2.5 , 2014 .

[10]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

[11]  António Pedro Souto,et al.  Application of nanotechnology in antimicrobial finishing of biomedical textiles , 2014 .

[12]  Paolo Pennacchi,et al.  A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions , 2013 .

[13]  Robert B. Randall,et al.  Simulating gear and bearing interactions in the presence of faults. Part I. The combined gear bearing dynamic model and the simulation of localised bearing faults , 2008 .

[14]  Yaguo Lei,et al.  A tacho-less order tracking technique for large speed variations , 2013 .

[15]  Robert B. Randall,et al.  Simulating gear and bearing interactions in the presence of faults Part II. Simulation of the vibrations produced by extended bearing faults , 2008 .

[16]  Robert B. Randall,et al.  Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine , 2009 .

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

[18]  Yaguo Lei,et al.  Tacholess Envelope Order Analysis and Its Application to Fault Detection of Rolling Element Bearings with Varying Speeds , 2013, Sensors.

[19]  Peter Nakaji,et al.  Case 1-6 , 2013 .

[20]  Carol Y. Espy-Wilson,et al.  Detection of Periodicity and Aperiodicity in Speech Signal Based on Temporal Information , 2003 .

[21]  Masakiyo Fujimoto,et al.  Noise robust voice activity detection based on periodic to aperiodic component ratio , 2010, Speech Commun..

[22]  P. Boersma ACCURATE SHORT-TERM ANALYSIS OF THE FUNDAMENTAL FREQUENCY AND THE HARMONICS-TO-NOISE RATIO OF A SAMPLED SOUND , 1993 .

[23]  Fanrang Kong,et al.  Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines , 2012 .

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

[25]  H. Karagülle,et al.  Simulation and analysis of vibration signals generated by rolling element bearing with defects , 2003 .

[26]  Roger F. Dwyer,et al.  Detection of non-Gaussian signals by frequency domain Kurtosis estimation , 1983, ICASSP.

[27]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .