New fault diagnosis approaches for detecting the bearing slight degradation
Author:
Funder
University of Guilan
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics
Link
http://link.springer.com/content/pdf/10.1007/s11012-019-01116-x.pdf
Reference44 articles.
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2. Meng L, Xiang J, Wang Y, Jiang Y, Gao H (2015) A hybrid fault diagnosis method using morphological filter–translation invariant wavelet and improved ensemble empirical mode decomposition. Mech Syst Signal Process 50:101–115. https://doi.org/10.1016/j.ymssp.2014.06.004
3. Liu X, Bo L, He X, Veidt M (2012) Application of correlation matching for automatic bearing fault diagnosis. J Sound Vib 331:5838–5852. https://doi.org/10.1016/j.jsv.2012.07.022
4. Jiang F, Zhu Z, Li W, Chen G, Zhou G (2013) Robust condition monitoring and fault diagnosis of rolling element bearings using improved EEMD and statistical features. Meas Sci Technol 25:025003. https://doi.org/10.1088/0957-0233/25/2/025003
5. McFadden PD, Smith JD (1984) Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review. Tribol Int 17:3–10. https://doi.org/10.1016/0301-679X(84)90076-8
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