Open Access
Issue
Mechanics & Industry
Volume 15, Number 6, 2014
Page(s) 517 - 524
DOI https://doi.org/10.1051/meca/2014058
Published online 20 October 2014
  1. R.B. Randall, J. Antoni, Rolling element bearing diagnostics – A tutorial, Mech. Syst. Signal Process. Elsevier (2011) 485–520 [Google Scholar]
  2. T. Karacay, N. Akturk, Experimental diagnostics of ball bearings using statistical and spectral methods, Tribol. Int. 42 (2009) 836–843 [CrossRef] [Google Scholar]
  3. N. Jamaludin, D. Mba, Monitoring Extremely Slow Rolling Element Bearings: Part I, NDT&E Int. Elsevier (2002) 349–358 [Google Scholar]
  4. Y. Zhang, H. Zuo, F. Bai, Classification of Fault Location and Performance Degradation or A Roller Bearing, Measurement Elsevier (2013) 1178–1189 [Google Scholar]
  5. 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 [Google Scholar]
  6. N.G. Nikolaou, I.A. Antoniadis, Rolling element bearing fault diagnosis using wavelet packets, NDT&E Int. Elsevier (2001) 197–205 [Google Scholar]
  7. J.C. Li, J. MA, Wavelet decomposition of vibrations for detection of bearing-localized defects. NDTE&E Int. (1997) 130–143 [Google Scholar]
  8. 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 [Google Scholar]
  9. Z.K. Peng, F.L. Chu, Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography, Mech. Syst. Signal Process. 18 (2004) 199–221 [CrossRef] [Google Scholar]
  10. J. Antoni, Fast computation of the kurtogram for the detection of transient faults, Mech. Syst. Signal Process. (2007) 108–124 [Google Scholar]
  11. Wei Guo, Peter W. Tse, 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 [CrossRef] [Google Scholar]
  12. B. Schölkopf, A. Smola, K.R Müller, Nonlinear Component Analysis as Kernel Eigenvalue Problem, Neural Comput. (1998) 1299–1319 [Google Scholar]
  13. 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 [Google Scholar]
  14. 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 [Google Scholar]
  15. 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 [Google Scholar]
  16. 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 [Google Scholar]
  17. S. Tufféry, Data Mining et statistique décisionnelle: L’intelligence dans les bases de données. Editions TECHNIP (2005) 133–171 [Google Scholar]
  18. P.-N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, 1st edition, Addison Wesley (2005) 487–548 [Google Scholar]
  19. M. Sayed-Mouchaweh, A. Devillez, G.V. Lecolier, P. Billaudel, Incremental learning in fuzzy pattern matching, Fuzzy Ssets and Systems Elsevier (2002) 49–62 [Google Scholar]
  20. M. Sayed-Mouchaweh, E. Lughofer, Learning in Non-Stationary Environments Springer, 2012 [Google Scholar]
  21. Q. Liu, M. Deng, Y. Shi, J. Wang, A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity, Comput. Geosci. 46 (2012) 296–309 [CrossRef] [Google Scholar]
  22. Z. Chen, Y.F. Li, Anomaly Detection Based on Enhanced DBScan Algorithm, Procedia Engineering 15 (2011) 178–182 [CrossRef] [Google Scholar]
  23. A.E. Brouwer, A.M. Cohen, A. Neumaier, Distance Regular Graphs. New York, Springer-Verlag (1989) 437 [Google Scholar]
  24. S. Kerroumi, X. Chiementin, L. Rasolofondraibe, Méthode de classification dynamique des indicateurs de défauts pour la surveillance des roulements, 3ème colloque AVE2012 (2012) [Google Scholar]
  25. A.K Jain, Robert P.W. Duin, and Jianchang Mao Statistical Pattern Recognition: A Review, Pattern Analysis and Machine Intelligence IEEE 22 (2000) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.