EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 330 (2025) EPJ Web Conf., 330 (2025) 05004 References
Open Access
Issue
EPJ Web Conf.
Volume 330, 2025
The 5th International Conference on Electrical Sciences and Technologies in the Maghreb (CISTEM 2024)
Article Number 05004
Number of page(s) 6
Section Power Quality Monitoring and Fault Diagnostic
DOI https://doi.org/10.1051/epjconf/202533005004
Published online 30 June 2025
  1. S. Fedala, D. Rémond, A. Felkaoui, et H. Selmani, « Intelligent Gear Fault Diagnosis in Normal and Non-stationary Conditions Based on Instantaneous Angular Speed, Differential Evolution and Multi-class Support Vector Machine », in Rotating Machinery and Signal Processing, vol. 12, A. Felkaoui, F. Chaari, et M. Haddar, Éd., in Applied Condition Monitoring, vol. 12., Cham: Springer International Publishing, 2019, p. 16‑33. doi: 10.1007/978-3-319-96181-1_2. [CrossRef] [Google Scholar]
  2. F. Bonnardot, K. Lizoul, S. Errafik, H. André, et F. Guillet, « High frequency demodulation technique for instantaneous angular speed estimation », Mech. Syst. Signal Process., vol. 159, p. 107745, oct. 2021, doi: 10.1016/j.ymssp.2021.107745. [CrossRef] [Google Scholar]
  3. R. Bertoni et H. Andre, « Comparison of Incremental Encoders in Order to Improve IAS Based Diagnosis », in 2020 Prognostics and Health Management Conference (PHM-Besançon), Besancon, France: IEEE, mai 2020, p. 157‑162. doi: 10.1109/PHM-Besancon49106.2020.00033. [Google Scholar]
  4. S. K. Roy, A. R. Mohanty, et C. S. Kumar, « Fault detection in a multistage gearbox by time synchronous averaging of the instantaneous angular speed », J. Vib. Control, vol. 22, no 2, p. 468‑480, févr. 2016, doi: 10.1177/1077546314533582. [CrossRef] [Google Scholar]
  5. Q. Zeng, G. Feng, Y. Shao, J. Devitt, F. Gu, et A. Ball, « An accurate instantaneous angular speed estimation method based on a dual detector setup », Mech. Syst. Signal Process., vol. 140, p. 106674, juin 2020, doi: 10.1016/j.ymssp.2020.106674. [CrossRef] [Google Scholar]
  6. Z. K. Peng et F. L. Chu, « Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography », Mech. Syst. Signal Process., vol. 18, no 2, p. 199‑221, mars 2004, doi: 10.1016/S0888-3270(03)00075-X. [CrossRef] [Google Scholar]
  7. Z. K. Peng, P. W. Tse, et F. L. Chu, « An improved Hilbert–Huang transform and its application in vibration signal analysis », J. Sound Vib., vol. 286, no 1‑2, p. 187‑205, août 2005, doi: 10.1016/j.jsv.2004.10.005. [CrossRef] [Google Scholar]
  8. N. E. Huang et al., « The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis », Proc. R. Soc. Lond. Ser. Math. Phys. Eng. Sci., vol. 454, no 1971, p. 903‑995, mars 1998, doi:10.1098/rspa.1998.0193. [CrossRef] [Google Scholar]
  9. Y. Lei, J. Lin, Z. He, et M. J. Zuo, « A review on empirical mode decomposition in fault diagnosis of rotating machinery », Mech. Syst. Signal Process., vol. 35, no 1‑2, p. 108‑126, févr. 2013, doi: 10.1016/j.ymssp.2012.09.015. [CrossRef] [Google Scholar]
  10. R. Ricci et P. Pennacchi, « Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions », Mech. Syst. Signal Process., vol. 25, no 3, p. 821‑838, avr. 2011, doi: 10.1016/j.ymssp.2010.10.002. [CrossRef] [Google Scholar]
  11. Z. K. Peng, P. W. Tse, et F. L. Chu, « A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing », Mech. Syst. Signal Process., vol. 19, no 5, p. 974‑988, sept. 2005, doi: 10.1016/j.ymssp.2004.01.006. [CrossRef] [Google Scholar]
  12. Z. Wu et N. E. Huang, « ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD », Adv. Adapt. Data Anal., vol. 01, no 01, p. 1‑41, janv. 2009, doi: 10.1142/S1793536909000047. [CrossRef] [Google Scholar]
  13. Y. Lei et M. J. Zuo, « Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs », Meas. Sci. Technol., vol. 20, no 12, p. 125701, déc. 2009, doi: 10.1088/0957-0233/20/12/125701. [CrossRef] [Google Scholar]
  14. C. Sun, H. Li, C. Xu, L. Ma, et H. Li, « Adaptively Optimized Masking EMD for Separating Intrinsic Oscillatory Modes of Nonstationary Signals », IEEE Signal Process. Lett., vol. 31, p. 216‑220, 2024, doi: 10.1109/LSP.2023.3347146. [CrossRef] [Google Scholar]
  15. A. Soualhi, K. Medjaher, et N. Zerhouni, « Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression », IEEE Trans. Instrum. Meas., vol. 64, no 1, p. 52‑62, janv. 2015, doi: 10.1109/TIM.2014.2330494. [CrossRef] [Google Scholar]
  16. W. Y. Liu, W. H. Zhang, J. G. Han, et G. F. Wang, « A new wind turbine fault diagnosis method based on the local mean decomposition », Renew. Energy, vol. 48, p. 411‑415, déc. 2012, doi: 10.1016/j.renene.2012.05.018. [CrossRef] [Google Scholar]
  17. Y. Wang, Z. He, et Y. Zi, « A Comparative Study on the Local Mean Decomposition and Empirical Mode Decomposition and Their Applications to Rotating Machinery Health Diagnosis », J. Vib. Acoust., vol. 132, no 2, p. 021010, avr. 2010, doi: 10.1115/1.4000770. [CrossRef] [Google Scholar]
  18. X. Song, Y. Zhang, et S. Zhang, « Research on Instantaneous Angular Speed Signal Separation Method for Planetary Gear Fault Diagnosis », Mod. Mech. Eng., vol. 14, no 02, p. 39‑50, 2024, doi: 10.4236/mme.2024.142004. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.