A Novel Ramanujan Digital Twin for Periodic Fault Feature Extraction of Rotating Machines
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49413-0_57
Reference10 articles.
1. Li, H., Liu, T., Wu, X., Chen, Q.: A bearing fault diagnosis method based on enhanced singular value decomposition. IEEE Trans. Industr. Inf. 17, 3220–3230 (2021)
2. Wang, L., Ma, S., Han, Q.K.: Enhanced sparse low-rank representation via nonconvex regularization for rotating machinery early fault feature extraction. IEEE-ASME Trans. Mechatron. 27, 3570–3578 (2022)
3. Zhao, D.Z., Cui, L.L., Liu, D.D.: Bearing weak fault feature extraction under time-varying speed conditions based on frequency matching demodulation transform. IEEE-ASME Trans. Mechatron. 28, 1627–1637 (2023)
4. Ding, J.K., Huang, L.P., Xiao, D.M., Li, X.J.: GMPSO-VMD algorithm and its application to rolling bearing fault feature extraction. Sensors 20 (2020)
5. Booyse, W., Wilke, D.N., Heyns, S.: Deep Digital Twins for detection, diagnostics and prognostics. Mech. Syst. Signal Process. 140, 106612 (2020)
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