Chinese Journal of Ship Research, Volume. 17, Issue 6, 111(2022)

Application of an improved EEMD method in bearing fault diagnosis of induction motors

Yong WU, Jianjun ZHU, and Ben ZOU
Author Affiliations
  • China Ship Development and Design Center, Wuhan 430064, China
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    References(15)

    [1] MAOUCHE Y, OUMAAMAR M E K, BOUCHERMA M et al. The propagation mechanism of fault signatures in squirrel cage induction motor drives[J]. Journal of Electrical Engineering & Technology, 14, 121-133(2019).

    [3] [3] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition the Hilbert spectrum f nonlinear nonstationary time series analysis[M]. Royal Society, 1998.

    [5] [5] ZHENG Y Y, ZHANG Y L, LIU Y L. The research of pulse wave signal denosing based on EMD ICA[C]2010 Third International Joint Conference on Computational Science Optimization. Huangshan, China: IEEE, 2010: 482485.

    [9] FEI S W. A hybrid model of EMD and multiple-kernel RVR algorithm for wind speed prediction[J]. International Journal of Electrical Power & Energy Systems, 78, 910-915(2016).

    [18] NIAZY R K, BECKMANN C F, BRADY J M et al. Performance evaluation of ensemble empirical mode decomposition[J]. Advances in Adaptive Data Analysis, 1, 231-242(2011).

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    Yong WU, Jianjun ZHU, Ben ZOU. Application of an improved EEMD method in bearing fault diagnosis of induction motors[J]. Chinese Journal of Ship Research, 2022, 17(6): 111

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    Paper Information

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    Received: Dec. 7, 2020

    Accepted: --

    Published Online: Mar. 26, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.02215

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