Diagnosis and Classifications of Bearing Faults Using Artificial Neural Network and Support Vector Machine
Author:
Publisher
Springer Science and Business Media LLC
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering,Ocean Engineering,Aerospace Engineering
Link
http://link.springer.com/content/pdf/10.1007/s40032-019-00519-9.pdf
Reference20 articles.
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2. S. Khanam, N. Tandon, J.K. Dutt, Fault size estimation in the outer race of ball bearing using discrete wavelet transform of the vibration signal. Procedia Technol. 14, 12–19 (2014). https://doi.org/10.1016/j.protcy.2014.08.003
3. S. Kulkarni, S.B. Wadkar, Experimental investigation for distributed defects in ball bearing using vibration signature analysis. Procedia Eng. 144, 781–789 (2016). https://doi.org/10.1016/j.proeng.2016.05.086
4. J.R. Stack, T.G. Habetler, R.G. Harley, Fault classification and fault signature production for rolling element bearings in electric machines. IEEE Trans. Ind. Appl. 40, 735–739 (2004)
5. P. Jayaswal, A.K. Wadhwani, K.B. Mulchandani, Machine fault signature analysis. Int. J. Rotating Mach. (2008). https://doi.org/10.1155/2008/583982
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