Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-10T14:20:45.432Z Has data issue: false hasContentIssue false
  • English
  • Français

Following the growth of a rolling fatigue spalling forpredictive maintenance

Published online by Cambridge University Press:  08 February 2013

Omar Djebili ,
Fabrice Bolaers ,
Ali Laggoun  and
Jean-Paul Dron
Omar Djebili*
Affiliation:
GRESPI, UFR, Sciences Exactes et Naturelles, Moulin de la Housse, BP 1039, 51687 Reims Cedex 2, France
Fabrice Bolaers
Affiliation:
GRESPI, UFR, Sciences Exactes et Naturelles, Moulin de la Housse, BP 1039, 51687 Reims Cedex 2, France
Ali Laggoun
Affiliation:
Departement de physique, Faculté des sciences UMBB, Boumerdes, Algerie
Jean-Paul Dron
Affiliation:
GRESPI, UFR, Sciences Exactes et Naturelles, Moulin de la Housse, BP 1039, 51687 Reims Cedex 2, France
*
a Corresponding author:omardjebili@yahoo.com
Get access

Abstract

The bearing is one of the most important components of rotating machines. Nevertheless,in normal conditions of use, it is subject to fatigue which creates a defect called arolling fatigue spalling. In this work, we present a follow-up of the thrust bearingfatigue on a test bench. Vibration analysis is a method used to characterize the defect.In order to obtain the fatigue curve more adjusted, we have studied the vibration levelaccording to statistical indicators: the Root Mean Square value (RMS value), which is oneof the best indicators to show the evolution of the bearing degradation. The approachfollows the working of the bearing until the degradation with an on line acquisition ofvibration statements in form of time signals. With the signal treatment, we obtain thevalues of the vibration amplitudes which characterize the vibration state of the bearing.Consequently, these values allow us to plot the fatigue curves. During our experimentalwork, this operation is applied for a batch of thrust bearings for which we have obtainedsimilar fatigue curves where the evolution trend follows a regression model from thedetection of the onset of the first spall. The result of this work will contribute topredict the working residual time before failure.

Keywords

Predictive maintenancebearingvibration analysisspallbearing fatigueMaintenance prédictiveroulementanalyse vibratoireécaillagefatigue de roulement
Type
Research Article
Information
Mechanics & Industry , Volume 14 , Issue 1 , 2013 , pp. 85 - 93
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

Alfredson, R.J., Mathew, J., Frequency domain methods for monitoring the condition of rolling element bearings, Mech. Eng. Trans. Inst. Eng. Aust. 2 (1985) 102–107 Google Scholar
Alfredson, R.J., Mathew, J., Time domain methods for monitoring the condition of rolling element bearings, Mech. Eng. Trans, Inst. Eng. Aust. 2 (1985) 108–112 Google Scholar
Burgess, P.F.J., Antifriction bearing fault detection using envelope detection, Trans, Inst. Prof., New Zealand-Elect. Mech. Chem. Eng. Sec. 15 (1998) 77–82 Google Scholar
Li, Y., Billington, S., Zhang, C., Dynamic prognostic prediction of defect propagation on rolling element bearing, Lubrification Eng. 42 (1999) 385–392 Google Scholar
A. Palmgren, Ball and Roller Bearing Engineering, Philadelphia, PA : Burbank, 1959
D. Nelias, Contribution à l’étude des roulements, Modélisation globale des roulements et avaries superficielles dans les contacts EHD pour des surfaces réelles ou indentées, Habilitation à diriger les recherches, Lyon : INSA de Lyon, 1999, 160 p.
Cheng, W., Cheng, H.S., Mura, T., Keer, L.M., Micro mechanics modeling of crack initiation under contact fatigue, ASME J. Tribol. 116 (1994) 2–8 CrossRefGoogle Scholar
Nelias, D., Dumont, M.L., Couhier, F., Dudragne, G., Flamand, L., Experimental and theorical investigation on rolling contact fatigue of 52100 and M50 steels under EHL or micro-EHL condition, ASME J. Tribol. 120 (1998) 184–190 CrossRefGoogle Scholar
Li, Y., Billigton, S., Zhang, C., Kurfess, T., Danyluk, S., Liang, S., Adaptive pronostics for rolling element bearing condition, Mech. Syst. Signal Proc. 13 (1999) 103–113 CrossRefGoogle Scholar
Li, Y., Kurfess, T.R., Liang, S.Y., Stochastic prognostics for rolling element bearings, Mech. Syst. Signal Proc. 14 (2000) 747–762 CrossRefGoogle Scholar
Shiroishi, J., Li, Y., Kurfess, T., Danyluk, S., Bearing condition diagnostics via vibration and acoustic emission measurements, Mech. Syst. Signal Proc. 11 (1997) 693–705 CrossRefGoogle Scholar
Qiu, J., Set, B.B., Liang, S.Y., Zhang, C., Damage mechanics approach for bearing lifetime prognostics, Mech. Syst. Signal Proc. 16 (2002) 817–829 CrossRefGoogle Scholar
Liu, T.I., Singonhalli, J.H., Detection of roller bearing defects using expert system and fuzzy logic, Mech. Syst. Signal Proc. 10 (1996) 595–614 CrossRefGoogle Scholar
Alguindigue, I.E., Loskiewicz-Buczak, A., Ubrig, R.E., Monitoring and diagnosis of rolling element bearings using artificial neural networks, IEEE Trans. Ind. Electron 40 (1993) 209–217 CrossRefGoogle Scholar
Shao, Y., Nezu, K., Prognosis of remaining bearing life using neural networks, Proc. Inst. Mech. Eng., J. Syst. Control. Eng. 214 (2000) 217–230 Google Scholar
Huanga, R., Xia, L., Lib, X., Liuc, C., Qiud, H., Richard, J.L., Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods, Mech. Syst. Signal Proc. 21 (2007) 193–207 CrossRefGoogle Scholar
Ray, A., Tangirala, S., Stochastic modeling of fatigue crack dynamics for on-line failure prognostics, IEEE Trans. Control Systems Technol. 4 (1996) 4 CrossRefGoogle Scholar
McFadden, P.D., Smith, J.D., Vibration monitoring of rolling element bearings by the high frequency resonance technique, Rev. Tribol. Int. 17 (1984) 3-10 CrossRefGoogle Scholar
R.B. Randall, J. Antoni, Rolling element bearing diagnostics, Mech. Sys. Signal Proc. (2011) 485–520
Williams, T., Ribadeneira, X., Rolling element bearing diagnostics in run-to-failure lifetime testing, Mech. Syst. Signal Proc. 15 (2001) 979–993 CrossRefGoogle Scholar
Qiu, H., Lee, J., Lin, J., Gang, Y., Robust performance degradation assessment methods for enhanced rolling element bearing prognostics, Advanced Engineering Informatics 17 (2003) 127–140 CrossRefGoogle Scholar
N. Gebraeel, M. Lawley, Residual life predictions from vibration-based degradation signals : A neural network approach, IEEE Trans. Industrial Elect. 51 (2004)
Rosado, L., Forster, N.H., Thomson, K.L., Cooke, J.W., Rolling contact fatigue life and spall propagation of AISI M50, M50NiL, and AISI 52100, Part I : Experimental Results, Tribol. Trans. 53 (2010) 29–41 CrossRefGoogle Scholar