Open Access
Issue
MATEC Web Conf.
Volume 77, 2016
2016 3rd International Conference on Mechanics and Mechatronics Research (ICMMR 2016)
Article Number 01024
Number of page(s) 5
Section Design and Study on Machinery
DOI https://doi.org/10.1051/matecconf/20167701024
Published online 03 October 2016
  1. W. Guo, P.W. Tse. A Djordjevich, Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition, Meas. 45 (2012) 1308–1322 [CrossRef] [Google Scholar]
  2. M. Zhao, J. Lin, X. Q. Xu, X. J. Li, Multi-fault detection of rolling element bearings under harsh working condition using IMF-based adaptive envelope order analysis, Sen 14(2014) 20320–20346 [CrossRef] [Google Scholar]
  3. W. T. Sui, S. Osman, W. Wang, An adaptive envelope spectrum technique for bearing fault detection, Meas. Sci. Tech, 25(2014) 095004 [CrossRef] [Google Scholar]
  4. J.P. Yang, S.X. Chen, Vibration predictions and verifications of disk drive spindle system with ball bearings. Comput Str. 80(2002)1409–18. [CrossRef] [Google Scholar]
  5. R. Aini, H. Rahnejat, R. Gohar, Vibration modeling of rotating spindles supported by lubricated bearings. J Tri. 124( 2002)158–65. [CrossRef] [Google Scholar]
  6. P. Konar, P. Chattopadhyay. Bearing fault detection of induction motor using wavelet and Support VectorMachines (SVMs). Appl. Soft Com 11 (2011) 4203–4211 [CrossRef] [Google Scholar]
  7. J. Yang, C.K. Peng, Y.S. Xu. Hierarchical entropy analysis for biological signals, J mput. Appl. Math 236 (2011) 728–742. [Google Scholar]
  8. H. Xu, G. Chen. An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO. Mech. Sys. Sig. Pro 35 (2013) 167–175 [CrossRef] [Google Scholar]
  9. B.T. Holm-Hansen, R.X. Gao, Vibration Analysis of a Sensor Integrated Ball Bearing. J Vib. Acou 122 (2000) 384–392 [CrossRef] [Google Scholar]
  10. J. S. Smith, The local mean decomposition and its application to EEG perception data, J. R. Soc. Int 2 (2005) 443–454. [CrossRef] [PubMed] [Google Scholar]
  11. Y Bu, J Wu, J Ma, et. al. The rolling bearing fault diagnosis based on LMD and LS-SVM. 26th CCDC 3797–3801 [Google Scholar]
  12. N. Cristianini, J. Shawe-Taylor. Support Vector Machines and other Kernel Based Learning Methods, Cambridge University Press, 2000 [CrossRef] [Google Scholar]
  13. Y. Wang, Z. He, Y. Zi, A demodulation method based on improved local mean decomposition and its application in rub-impact fault diagnosis. Meas. Sci. Tech. 20(2009)025704 [CrossRef] [Google Scholar]
  14. J. Chenga, D. Yua, J. Tangb, Y. Yanga, Application of SVM and SVD technique based on EMD to the fault diagnosis of the rotating machinery. Shoc. Vib. 16(2009) 89–98 [CrossRef] [Google Scholar]
  15. J. Lee. Advanced Electrical and Electronics Engineering, Springer Science & Business Media, 2(2011) 425 [Google Scholar]
  16. Y. Yang, D. Yu, J. Cheng. A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Meas. 40 (2007) 943–950 [CrossRef] [Google Scholar]
  17. S. Nandi, H. A. Toliyat, X. D. Li, A. Richard, condition monitoring and fault diagnosis of electrical motors-a review, IEEE T Energy Conver, 20(2005) 719–729. [CrossRef] [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.