A fault diagnosis method of rolling bearing based on VMD Tsallis entropy and FCM clustering
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-020-09534-w.pdf
Reference43 articles.
1. Ahmed HOA, Nandi AK (2019) Three-stage hybrid fault diagnosis for rolling bearings with compressively sampled data and subspace learning techniques. IEEE Trans Ind Electron 66(7):5516–5524
2. Akhand R, Upadhyay SH (2016) A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribology International 96:289–306
3. Brkovic A, Gajic D, Gligorijevic J, Savic-Gajic I, Georgieva O, Gennaro SD (2017) Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery. Energy 136:63–71
4. Case Western Reserve University Bearing Data Center n.d.. [Online]. Avail-able: http://csegroup.case.edu/bearingdatacenter/home
5. Cerrada M, Sanchez RV, Li C, Pacheco F, Cabrera D, Oliveira JVD, Rafael EV (2018) A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems & Signal Processing 99:169–196
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