Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-13T06:11:00.719Z Has data issue: false hasContentIssue false

Vibratory monitoring of a spalling bearing defect in variable speed regime

Published online by Cambridge University Press:  12 June 2013

Khalid Ait Sghir*
Affiliation:
Groupe de Recherche en Science Pour l’Ingénieur, Université de Reims Champagne Ardenne,UFR Sciences Exactes et Naturelles ; Moulin de la Housse, BP 1039, 51687 Reims Cedex, France
Fabrice Bolaers
Affiliation:
Groupe de Recherche en Science Pour l’Ingénieur, Université de Reims Champagne Ardenne,UFR Sciences Exactes et Naturelles ; Moulin de la Housse, BP 1039, 51687 Reims Cedex, France
Olivier Cousinard
Affiliation:
Groupe de Recherche en Science Pour l’Ingénieur, Université de Reims Champagne Ardenne,UFR Sciences Exactes et Naturelles ; Moulin de la Housse, BP 1039, 51687 Reims Cedex, France Société Altéad Industrie Est, 11 rue du colonel Charbonneaux, 51100 Reims, France
Jean-Paul Dron
Affiliation:
Groupe de Recherche en Science Pour l’Ingénieur, Université de Reims Champagne Ardenne,UFR Sciences Exactes et Naturelles ; Moulin de la Housse, BP 1039, 51687 Reims Cedex, France
*
aCorresponding author: khalid.ait-sghir@univ-reims.fr
Get access

Abstract

Rotating machines monitoring in a non-stationary regime, characterized by the operating parameters (speed, load) variation, presents a particular challenge. The speed variation has an impact on the vibratory response given by the accelerometers and therefore masks any defect that may be detected by classical indicators (for example RMS value). To overcome this problem, a new indicator is proposed. For this, a signal accelerometer and a signal from an optical encoder are acquired simultaneously. An algorithm to estimate the instantaneous speed from the signal delivered by the optical encoder is applied. Then, each sample of the accelerometer signal is divided by its corresponding instantaneous speed sample. The RMS value is then applied to the resulting signal. A model simulation signal is used to test the proposed method. A test rig is performed to extract signals of different degradation states of thrust bearings in variable speed regime. The results show a correlation between the proposed RMS value and the thrust bearings state in variable regime.

Type
Research Article
Copyright
© AFM, EDP Sciences 2013

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

B.T. Kuhnell, Wear in rolling element bearings and gears – how age and contamination affect them, Machinery Lubrication Magazine, Monash University, 2004
Tandon, N., Nakra, B.C., Detection of defects in rolling element bearings by vibration monitoring, J. Institution of Engineers (India)- Mechanical Engineering Division (ISSN 0020-3408), 73 (1993) 271282 Google Scholar
R.A Collacott, Mechanical fault diagnosis, Chapman and Hall, London, 1977
Jardine, 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
Tandon, N., Choudury, 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
McFadden, PD., Smith, J.D. Vibration monitoring of rolling element bearings by the high frequency resonance technique – a review. Tribol. Int. 17 (1984) 310 CrossRefGoogle Scholar
McFadden, PD., Smith, J.D., Model for the vibration produced by a single point defect in a rolling element bearing, J. Sound Vibr. 96 (1984) 6982 CrossRefGoogle Scholar
McFadden, PD., Smith, J.D., The vibration produced by multiple point defects in a rolling element bearing, J. Sound Vibr. 98 (1985) 263273 CrossRefGoogle Scholar
Zhu, Z.K., Yan, R.Q., Luo, L.H., Feng, Z.H., Kong, F.R., Detection of signal transients based on wavelet and statistics for machine fault diagnosis, Mech. Syst. Signal Process. 23 (2009) 10761097 CrossRefGoogle Scholar
Randall, R.B., Antoni, J., Rolling element bearing diagnostics-A tutorial, Mech. Syst. Signal Process. 25 (2011) 485520 CrossRefGoogle Scholar
Antoni, J., Cyclic spectral analysis in practice, Mech. Syst. Signal Process. 21 (2007) 597630 CrossRefGoogle Scholar
Wang, H., Chen, P., Fuzzy diagnosis method for rotating machinery in variable rotating Speed, IEEE Sensors J. 11 (2011) 2334
Bartelmus, W., Zimroz, R., A new feature for monitoring the condition of gearboxes in non-stationary operating conditions, Mech. Syst. Signal Process. 23 (2009) 15281534 CrossRefGoogle Scholar
McBain, J., Timusk, M., Fault detection in variable speed machinery: Statistical parameterization, J. Sound Vibr. 237 (2009) 623646 CrossRefGoogle Scholar
Tandon, N., Choudury, A., An analytical model for the prediction of the vibration response of rolling element bearings due to localized defect, J. Sound Vibr. 205 (1997) 275292 CrossRefGoogle Scholar
C. Zhang, Defect detection and life prediction of rolling element bearings, Thesis, Georgia Institue of Technology, Chapt. V, pp. 97–123, 2001
F. Bolaers, S. Rémond, X. Chiementin, S. Crequey, J.P. Dron, Modélisation de la force d’impact due à un écaillage de fatigue dans les roulements, Premier Colloque International IMPACT 2010, Djerba, Tunisie, 2010
Antoni, J., Randall, R.B., Differential diagnosis of gear and bearing faults, ASME J. Vibr. Acoust. 124 (2002) 165171 CrossRefGoogle Scholar
Antoni, J., R.B. Randall, The spectral kurtosis: a useful tool for characterizing non-stationary signals. Mech. Syst. Signal Process. 20 (2006) 282307 Google Scholar