Open Access
Issue
MATEC Web Conf.
Volume 90, 2017
The 2nd International Conference on Automotive Innovation and Green Vehicle (AiGEV 2016)
Article Number 01006
Number of page(s) 9
DOI https://doi.org/10.1051/matecconf/20179001006
Published online 20 December 2016
  1. Tandon, N., A. Choudhury, A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International. 32(8): p. 469–480.(1999) [CrossRef] [Google Scholar]
  2. Rai, A., S.H. Upadhyay, A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribology International. 96: p. 289–306.(2016) [CrossRef] [Google Scholar]
  3. Lacey, S.J., An Overview of Bearing Vibration Analysis. Maintenance & asset management. 23(6): p. 11.(2008) [Google Scholar]
  4. Dron, J.P., F. Bolaers, l. Rasolofondraibe, Improvement of the sensitivity of the scalar indicators (crest factor, kurtosis) using a de-noising method by spectral subtraction: application to the detection of defects in ball bearings. Journal of Sound and Vibration. 270(1–2): p. 61–73.(2004) [CrossRef] [Google Scholar]
  5. Harvey, T.J., R.J.K. Wood, Powrie, H.E.G., Electrostatic wear monitoring of rolling element bearings. Wear. 263(7–12): p. 1492–1501.(2007) [CrossRef] [Google Scholar]
  6. Pachaud, C., R. Salvetat, C. Fray, crest factor and kurtosis contributions to identify defects inducing periodical impulsive forces. Mechanical Systems and Signal Processing. 11(6): p. 903–916.(1997) [CrossRef] [Google Scholar]
  7. Zhi-qiang, Z., et al., Investigation of rolling contact fatigue damage process of the coating by acoustics emission and vibration signals. Tribology International. 47: p. 25–31.(2012) [CrossRef] [Google Scholar]
  8. Karacay, T., N. Akturk, Experimental diagnostics of ball bearings using statistical and spectral methods. Tribology International 42((2009) ): p. 836–843.( 2009) [CrossRef] [Google Scholar]
  9. Choudhury, A., D. Paliwal, Application of Frequency B-Spline Wavelets for Detection of Defects in Rolling Bearings. Procedia Engineering. 144: p. 289–296.(2016) [CrossRef] [Google Scholar]
  10. He, W., et al., Health monitoring of cooling fan bearings based on wavelet filter. Mechanical Systems and Signal Processing. 64–65: p. 149–161.(2015) [CrossRef] [Google Scholar]
  11. Mishra, C., A.K. Samantaray, G. Chakraborty, Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate. Mechanical Systems and Signal Processing. 72–73: p. 206–222.(2016) [CrossRef] [Google Scholar]
  12. Wang, Y., et al., Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis. Mechanical Systems and Signal Processing. 54–55: p. 259–276.(2015) [CrossRef] [Google Scholar]
  13. Dybała, J., R. Zimroz, Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal. Applied Acoustics. 77: p. 195–203.(2014) [CrossRef] [Google Scholar]
  14. Xue, X., et al., An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis. Mechanical Systems and Signal Processing. 62–63: p. 444–459.(2015) [CrossRef] [Google Scholar]
  15. Zhang, X., J. Zhou, Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mechanical Systems and Signal Processing. 41(1–2): p. 127–140.(2013) [CrossRef] [Google Scholar]
  16. Zhao, M., et al., Fault diagnosis of rolling element bearings via discriminative subspace learning: Visualization and classification. Expert Systems with Applications. 41(7): p. 3391–3401.(2014) [CrossRef] [Google Scholar]
  17. Gan, M., C. Wang, C.a. Zhu, Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mechanical Systems and Signal Processing. 72–73: p. 92–104.(2016) [CrossRef] [Google Scholar]
  18. Li, X., et al., Rolling element bearing fault detection using support vector machine with improved ant colony optimization. Measurement. 46(8): p. 2726–2734.(2013) [CrossRef] [Google Scholar]
  19. Liu, Z., et al., Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings. Neurocomputing. 99: p. 399–410.(2013) [CrossRef] [Google Scholar]
  20. Tian, Y., et al., Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine. Mechanism and Machine Theory. 90: p. 175–186.(2015) [CrossRef] [Google Scholar]
  21. M, S., et al., International Conference on Design and Manufacturing (IConDM2013)ANN based Evaluation of Performance of Wavelet Transform for Condition Monitoring of Rolling Element Bearing. Procedia Engineering. 64: p. 805–814.(2013) [CrossRef] [Google Scholar]
  22. Saidi, L., J. Ben Ali, F. Fnaiech, Application of higher order spectral features and support vector machines for bearing faults classification. ISA Transactions. 54: p. 193–206.(2015) [CrossRef] [Google Scholar]
  23. Abdi, Encyclopedia of Measurements and Statistics. 2007, Dallas, USA. [Google Scholar]
  24. Mao, J., A. Jain, A self organizing network for hyperellipsoidal clustering (HEC). IEEE Transaction on Neural Network. 7(1): p. 16–29.(1996) [CrossRef] [Google Scholar]
  25. De Maesschalck, R., D. Jouan-Rimbaud, D.L. Massart, The Mahalanobis distance. Chemometrics and Intelligent Laboratory Systems. 50(1): p. 1–18.(2000) [CrossRef] [Google Scholar]
  26. Smith, L., A Tutorial On Principle Components Analyis, in A Tutorial On Principle Components Analyis. 2002. [Google Scholar]

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