Rolling Element Bearing Fault Diagnosis Using Hybrid Machine Learning Models
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-40455-9_33
Reference23 articles.
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2. Wang, D., Tse, P.W., Tsui, K.L.: An enhanced Kurtogram method for fault diagnosis of rolling element bearings. Mech. Syst. Signal Process. 35, 176–199 (2013). https://doi.org/10.1016/J.YMSSP.2012.10.003
3. Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A.454, 903–995 (1998)
4. Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. (2013). https://doi.org/10.1109/TSP.2013.2288675
5. Braut, S., Žigulić, R., Skoblar, A., Štimac Rončević, G.: Partial Rub Detection Based on Instantaneous Angular Speed Measurement and Variational Mode Decomposition. J. Vibr. Eng. Technol. 8(2), 351–364 (2019). https://doi.org/10.1007/s42417-019-00177-2
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