Approximate Entropy Analysis of the Acoustic Emission From Defects in Rolling Element Bearings

This paper introduces approximate entropy (ApEn) to address a nonlinear feature parameter of acoustic emission (AE) signal for the defect detection of rolling element bearings. With respect to AE signal, parameter selection of ApEn calculation is investigated, and appropriate parameters are suggested. Finally, an experimental study is presented to investigate the influence of various running conditions, i.e., radial load, rotating speed and defect size, on ApEn calculation. The results demonstrate that ApEn provides an effective measure for AE analysis and can be used as an effective feature parameter of AE signal for the defect detection of rolling element bearings.

[1]  Peter W. Tse,et al.  Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities , 2001 .

[2]  I. Grabec,et al.  Simulation of AE signals and signal analysis systems , 1985 .

[3]  B. C. Nakra,et al.  TECHNICAL ARTICLE practical articles in shock and vibration technology: Vibration and Acoustic Monitoring Techniques for the Detection of Defects in Rolling Element Bearings -- a Review , 1992 .

[4]  Xu Yong,et al.  APPROXIMATE ENTROPY AND ITS APPLICATIONS IN MECHANICAL FAULT DIAGNOSIS , 2002 .

[5]  D Mba,et al.  Bearing defect diagnosis and acoustic emission , 2003 .

[6]  Juan José González de la Rosa,et al.  Higher Order Statistics and Independent Component Analysis for Spectral Characterization of Acoustic Emission Signals in Steel Pipes , 2007, IEEE Transactions on Instrumentation and Measurement.

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

[8]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Roberto Hornero,et al.  Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy , 2005, Clinical Neurophysiology.

[10]  Christophe Pierre,et al.  PREDICTING LOCALIZATION VIA LYAPUNOV EXPONENT STATISTICS , 1997 .

[11]  Michael I. Friswell,et al.  Defect Diagnosis for Rolling Element Bearings Using Acoustic , 2009 .

[12]  Li Lin,et al.  Approximate entropy as acoustic emission feature parametric data for crack detection , 2011 .

[13]  K. Ono,et al.  Analysis of Ball Bearing Vibrations Caused by Outer Race Waviness , 1998 .

[14]  C. Tai,et al.  A correlated empirical mode decomposition method for partial discharge signal denoising , 2010 .

[15]  Martine Wevers,et al.  Modal analysis of acoustic emission signals from CFRP laminates , 1999 .

[16]  Arthur W. Lees,et al.  Detection of severe sliding and pitting fatigue wear regimes through the use of broadband acoustic emission , 2005 .

[17]  Leonard A. Smith Intrinsic limits on dimension calculations , 1988 .

[18]  Steven M. Pincus Assessing Serial Irregularity and Its Implications for Health , 2001, Annals of the New York Academy of Sciences.

[19]  B. Subramanyam,et al.  Analysis of Acoustic Emission Signals using WaveletTransformation Technique , 2008 .

[20]  Alberto Rolo-Naranjo,et al.  A method for the correlation dimension estimation for on-line condition monitoring of large rotating machinery , 2005 .

[21]  R. Such,et al.  Estimation of bearing defect size with acoustic emission , 2004 .

[22]  Qian Qing-quan Application of Approximate Entropy to Fault Signal Analysis in Electric Power System , 2008 .

[23]  Fulei Chu,et al.  Modal Analysis of Rubbing Acoustic Emission for Rotor-Bearing System Based on Reassigned Wavelet Scalogram , 2008 .

[24]  Robert X. Gao,et al.  Mechanical Systems and Signal Processing Approximate Entropy as a Diagnostic Tool for Machine Health Monitoring , 2006 .

[25]  David Mba,et al.  Development of Acoustic Emission Technology for Condition Monitoring andDiagnosis of Rotating Machines; Bearings, Pumps, Gearboxes, Engines and RotatingStructures. , 2006 .

[26]  M. Elforjani,et al.  Detecting the onset, propagation and location of non-artificial defects in a slow rotating thrust bearing with acoustic emission , 2008 .

[27]  Siti Anom Ahmad,et al.  Moving approximate entropy applied to surface electromyographic signals , 2008, Biomed. Signal Process. Control..

[28]  Mehdi Behzad,et al.  Defect size estimation in rolling element bearings using vibration time waveform , 2009 .

[29]  Antolino Gallego,et al.  Wavelet power, entropy and bispectrum applied to AE signals for damage identification and evaluation of corroded galvanized steel , 2009 .