Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-17T21:37:37.964Z Has data issue: false hasContentIssue false
  • English
  • Français

Online classification for spalling detection and vibratorybehavior monitoring

Published online by Cambridge University Press:  20 October 2014

Sanaa Kerroumi ,
Xavier Chiementin  and
Lanto Rasolofondraibe
Sanaa Kerroumi*
Affiliation:
CReSTIC, University of Reims Champagne Ardenne, Moulin de la Housse, 51687 Reims Cedex 2, France
Xavier Chiementin
Affiliation:
GRESPI, University of Reims Champagne Ardenne, Moulin de la Housse, 51687 Reims Cedex 2, France
Lanto Rasolofondraibe
Affiliation:
CReSTIC, University of Reims Champagne Ardenne, Moulin de la Housse, 51687 Reims Cedex 2, France
*
a Corresponding author:sanaa.kerroumi@univ-reims.fr
Get access

Abstract

Vibration analysis is the most widely used tool in industrial application for machine’shealth condition assessment. Bearings, however, are very sensitive and require specialattention. Vibration analysis employs various signal processing methods such as spectralanalysis, time-scale, time frequency analysis, etc. these methods are used to analyzebearings’ vibratory behavior by monitoring the evolution of statisticalindicators.However, diagnosing the bearing depending on traditional features only isn’tsufficient to assure effective or reliable assessment of the component’ health condition.This paper proposes a multi-features online dynamic classification as a new method forfault detection and health condition monitoring for bearings; this technique uses multiplefeatures, including traditional features extracted from the raw signal, two specialfeatures extracted by wavelet analysis, the spectral kurtosis, coupled with a nonlinearprincipal component analysis and a dynamic classification to capitalize on the hiddeninformation in the time evolution of the features.Through this article, we introducedifferent measures and techniques used to characterize the health state of rolling, thenwe deploy a methodology using dynamic classification to detect early defect. To ensure analmost continuous surveillance, this methodology is based on a real-time analysis, anduses specific statistical indicators adapted to the experimental bench. Then, themonitoring of the degradation is achieved through the resulting class of the state ofdegradation. New parameters such as the speed of the class, the position of the class, theshape of the class will be discussed to inform the state of damage. The suggestedmethodology is validated by analyzing several fatigue tests from a fatigue bench bearingthrust ball referenced SNR51207.

Keywords

Online classificationbearingswavelet analysisKernel principal components analysisspectral kurtosis.
Type
Research Article
Information
Mechanics & Industry , Volume 15 , Issue 6 , 2014 , pp. 517 - 524
Copyright
© AFM, EDP Sciences 2014

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

R.B. Randall, J. Antoni, Rolling element bearing diagnostics – A tutorial, Mech. Syst. Signal Process. Elsevier (2011) 485–520
Karacay, T., Akturk, N., Experimental diagnostics of ball bearings using statistical and spectral methods, Tribol. Int. 42 (2009) 836–843 CrossRefGoogle Scholar
N. Jamaludin, D. Mba, Monitoring Extremely Slow Rolling Element Bearings: Part I, NDT&E Int. Elsevier (2002) 349–358 CrossRef
Y. Zhang, H. Zuo, F. Bai, Classification of Fault Location and Performance Degradation or A Roller Bearing, Measurement Elsevier (2013) 1178–1189
Yang Yu, Yu Dejie, C. Junsheng, A Roller Bearing Fault Diagnosis Method Based On EMD Energy Entropy and ANN, J. Sound Vibr. Elsevier (2006) 269–277
N.G. Nikolaou, I.A. Antoniadis, Rolling element bearing fault diagnosis using wavelet packets, NDT&E Int. Elsevier (2001) 197–205
J.C. Li, J. MA, Wavelet decomposition of vibrations for detection of bearing-localized defects. NDTE&E Int. (1997) 130–143
K. Mori, N. Kasashima, T. Yoshika, Y. Ueno, Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals, Wear (1996) 162–195
Peng, Z.K., Chu, F.L., Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography, Mech. Syst. Signal Process. 18 (2004) 199–221 CrossRefGoogle Scholar
J. Antoni, Fast computation of the kurtogram for the detection of transient faults, Mech. Syst. Signal Process. (2007) 108–124
Wei Guo, Peter Tse, W., Alexandar Djordjevich, Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition, Measurement 45 (2012) 1308–1322 Google Scholar
B. Schölkopf, A. Smola, K.R Müller, Nonlinear Component Analysis as Kernel Eigenvalue Problem, Neural Comput. (1998) 1299–1319
M. Boumahdi, J. Dronb, S. Rechakc, O. Cousinard, On the Extraction of Rules in the Identification of Bearing Defects In Rotating Machinery Using Decision Tree, Expert Systems with Applications Elsevier (2010) 5887–5894
Yujing Wang, S. Kang, Y. Jiang, G. Yang, Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine, Mech. Syst. Signal Process. Elsevier (2012) 404–414
C.T. Yiakopoulos, K.C. Gryllias, I.A. Antoniadis, Rolling Element Bearing Fault Detection in Industrial Environments Based On K-Means Clustering Approach, Expert Syst. Appl. Elsevier (2011) 2888–2911
J.J. González de la Rosaa, A. Moreno Muñoz, Higher-Order Cumulants And Spectral Kurtosis For Early Detection Of Subterranean Termites, Mech Syst. Signal Process. Elsevier (2008) 279–294
S. Tufféry, Data Mining et statistique décisionnelle: L’intelligence dans les bases de données. Editions TECHNIP (2005) 133–171
P.-N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, 1st edition, Addison Wesley (2005) 487–548
M. Sayed-Mouchaweh, A. Devillez, G.V. Lecolier, P. Billaudel, Incremental learning in fuzzy pattern matching, Fuzzy Ssets and Systems Elsevier (2002) 49–62
M. Sayed-Mouchaweh, E. Lughofer, Learning in Non-Stationary Environments Springer, 2012
Liu, Q., Deng, M., Shi, Y., Wang, J., A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity, Comput. Geosci. 46 (2012) 296–309 CrossRefGoogle Scholar
Chen, Z., Li, Y.F., Anomaly Detection Based on Enhanced DBScan Algorithm, Procedia Engineering 15 (2011) 178–182 CrossRefGoogle Scholar
A.E. Brouwer, A.M. Cohen, A. Neumaier, Distance Regular Graphs. New York, Springer-Verlag (1989) 437
S. Kerroumi, X. Chiementin, L. Rasolofondraibe, Méthode de classification dynamique des indicateurs de défauts pour la surveillance des roulements, 3ème colloque AVE2012 (2012)
A.K Jain, Robert P.W. Duin, and Jianchang Mao Statistical Pattern Recognition: A Review, Pattern Analysis and Machine Intelligence IEEE 22 (2000)