Refined Composite Multiscale Range Entropy and Pairwise Feature Proximity-Based Fault Detection Method of Rotating Machinery
Author:
Publisher
Springer Science and Business Media LLC
Subject
Microbiology (medical),Immunology,Immunology and Allergy
Link
https://link.springer.com/content/pdf/10.1007/s42417-022-00679-6.pdf
Reference26 articles.
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2. Shao H, Jiang H, Zhao H, Wang F (2017) A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mech Syst Signal Process 95:187–204
3. Rai A, Upadhyay S (2016) A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribol Int 96:289–306
4. Barbosh M, Singh P, Sadhu A (2020) Empirical mode decomposition and its variants: a review with applications in structural health monitoring. Smart Mater Struct 29(9):093001
5. Isham MF, Leong MS, Lim M, Zakaria M (2019) A review on variational mode decomposition for rotating machinery diagnosis. In: MATEC Web of Conferences. vol. 255. EDP Sciences. p. 02017
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