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Cyclostationarity applied to acoustic emission and developmentof a new indicator for monitoring bearing defects

Published online by Cambridge University Press:  16 September 2014

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Abstract

The exploitation of cyclostationarity properties of vibratory signals is now more widelyused for monitoring rotating machinery and especially for diagnosing bearing defects. Theacoustic emission (AE) technology has also emerged as a reliable tool for preventivemaintenance of rotating machines. In this study, we propose an experimental study thatcharacterizes the cyclostationary aspect of acoustic emission (AE) signals recorded from adefective bearing (40 μm on the outer race) to see its efficiency to detecta defect at its very early stage of degradation. An industrial sensor (UE10 000) is used.An electrical circuit converts the high frequency signal into an audible signal byheterodyning. The cyclic spectral density, which is a tool dedicated that put intoevidence the presence of cyclostationarity, is used for characterizing thecyclostationary. Two new indicators based on this cyclostationary technique are proposedand compared for early detection of defective bearings.

Type
Research Article
Copyright
© AFM, EDP Sciences 2014

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References

Jardine, A.K.S., Lin, D., Banjevic, D., A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mech. Syst. Signal Process 20 (2006) 14831510 CrossRefGoogle Scholar
S. Sassi, B. Badri, M. Thomas, Tracking surface degradation of ball bearings by means of new time domain scalar descriptors, Int. J. COMADEM 11 (2008) ISSN1363-7681 36–45
Tandon, N., Choudhury, A., A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings, Tribol. Int. 32 (1999) 469480 CrossRefGoogle Scholar
Choudhury, A., Tandon, N., Application of acoustic emission technique for the detection of defects in rolling element bearings, Tribol. Int. 33 (2000) 3945 CrossRefGoogle Scholar
X.Z. Yongyong He, I. Michael, Friswell, Defect diagnosis for rolling element bearings using acoustic emission, J. Vib. Acoust. 131 (2009) (ASME)
Dadouche, A., et al., Sensitivity of Air-Coupled Ultrasound and Eddy Current Sensors to Bearing Fault Detection, Tribol. Trans. 51 (2008) 310323 CrossRefGoogle Scholar
Shiroishi, J. et al., Bearing condition diagnosis via vibration and acoustic emission measurements, Mech. Syst. Signal Process. 11 (1997) 693705 CrossRefGoogle Scholar
Y.-H. Kim et al., Condition Monitoring of Low Speed Bearings: A Comparative Study of the Ultrasound Technique Versus Vibration Measurements, in Engineering Asset Management, Springer London, 2006, pp. 182–191
M. Kedadouche, M. Thomas, A. Tahan, Monitoring bearings by acoustic emission: a comparative study with vibration techniques for early detection, Proceedings of the 30th Seminar on machinery vibration, Canadian Machinery Vibration Association, Niagara Falls (ON, Canada), 2012, 17 p.
X. Chiementin, D. Mba, B. Charnley, S. Lignon, J.-P. Dron, Effect of the denoising on acoustic emission signals, J. Vib. Acoust. 132 (2010)
Liao, C., Li, X., Liu, D., Application of reassigned wavelet scalogram in feature extraction based on acoustic emission signal, J. Mech. Eng. 45 (2009) 273279 CrossRefGoogle Scholar
Zvokelj, M., Zupan, S., Prebil, I., Multivariate and multiscale monitoring of large-size low-speed bearings using ensemble empirical mode decomposition method combined with principal component analysis, Mech. Syst. Signal Process. (24) (2010) 10491067 CrossRefGoogle Scholar
Kilundu, B., et al., Cyclostationarity of Acoustic Emissions (AE) for monitoring bearing defects, Mech. Syst. Signal Process. 25 (2011) 20612072 CrossRefGoogle Scholar
Antoni, J., Cyclostationarity by examples, Mech. Syst. Signal Process. 23 (2009) 9871036 CrossRefGoogle Scholar
J. Antoni, F. Bonnardot, A. Raad, M. El Badaoui, Cyclostationary modelling of rotating machine vibration signals, Mech. Syst. Signal Process. (2004) 1285–1314
Bonnardot, F., Randall, R.B., Guillet, F., Extraction of 2nd order cyclostationary sources–application to vibration analysis, Mech. Syst. System Process. 19 (2005) 12301244 CrossRefGoogle Scholar
Boustany, R., Antoni, J., A subspace method for the blind extraction of a cyclostationary source: application to rolling element bearing diagnostics, Mech. Syst. Syst. Process. 19 (2005) 12451259 CrossRefGoogle Scholar
R. Boustany, J. Antoni, Blind extraction of a cyclostationary signal using reduced-rank cyclic regression-A unifying approach, Mech. Syst. Syst. Process. (2008) 520–541
M. Thomas, J. Masounave, T.M. Dao, C.T. Le Dinh, F. Lafleur, Rolling element bearing degradation and vibration signature relationship, 2e Conférence Internationale sur les méthodes de surveillance et techniques de diagnostics acoustiques et vibratoires, SFM, Senlis, 1995, Vol. 1, pp. 267–277
J.I. Taylor, Identification of bearing defects by spectral analysis, J. Mech. Design 102 (1980)
Antoni, J., Cyclic spectral analysis in practice, Mech. Syst. Signal Process. 21 (2007) 597630 CrossRefGoogle Scholar
Antoni, J., Cyclic spectral analysis of rolling element bearing signals: facts and fictions, J. Sound Vib. 304 (2007) 497529 CrossRefGoogle Scholar