A Secondary Iterative Sifting EMD Algorithm and its Application to Bearing Fault Diagnosis

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Abstract:

Aiming at the mode mixing problem caused by interpolation point selection of conventional EMD (Empirical mode decomposition) method, a secondary iterative sifting EMD method that can avoid mode mixing and achieve high-precision decomposition of HHT (Hilbert–Huang transformation) is proposed based on the theory of EMD. The simulation results show that the proposed method is superior to conventional EMD on the ability to split mixed signal. Finally, the proposed algorithm is applied to the fault diagnosis of rolling bearing and the test results have proved its effectiveness and advantages.

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451-458

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February 2015

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] P. Frank Pai. Detection and identification of nonlinearities by amplitude and frequency modulation analysis[J]. Mechanical Systems and Signal Processing, 2008, 22: 1107-1132.

DOI: 10.1016/j.ymssp.2007.11.006

Google Scholar

[2] Banfu Yan. A Comparative Study of Modal Parameter Identification Based on Wavelet and Hilbert–Huang Transforms [J]. Computer-Aided Civil and Infrastructure Engineering, 2006, (21): 9-23.

DOI: 10.1111/j.1467-8667.2005.00413.x

Google Scholar

[3] Changzheng Chen, Lixin Hu, Bo Zhou. Equipment vibration analysis and fault diagnosis technology [M]. Science press, (2007).

Google Scholar

[4] Biao Huang, Guozheng Yan. Analysis of the characteristics of gastrointestinal motility based on Hilbert-Huang transform method [J]. High Technology Letters, 2008, (14): 30-34.

Google Scholar

[5] Li H L, Deng x Y-Dai H L. Structural Damage Detection Using the Combination Method of EMD and Wavelet Analysis [J]. Mechanical Systems and Signal Processing, 2007, 211: 298-306.

DOI: 10.1016/j.ymssp.2006.05.001

Google Scholar

[6] Y. Kopsinis and S. McLaughlin. Investigation and performance enhancement of the empirical mode decomposition method based on a heuristic search optimization approach [J]. IEEE Transactions on Signal Processing, 2008, 56(1).

DOI: 10.1109/tsp.2007.901155

Google Scholar

[7] Hongkai Jiang, Chengliang Li, Huaxing Li. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis [J]. Mechanical Systems and Signal Processing, 2013, (36): 225-239.

DOI: 10.1016/j.ymssp.2012.12.010

Google Scholar

[8] Yaguo Lei, Jing Lin, Zhengjia He, Ming J. Zuo. A review on empirical mode decomposition in fault diagnosis of rotating machinery [J]. Mechanical Systems and Signal Processing, 2013, (35): 108-126.

DOI: 10.1016/j.ymssp.2012.09.015

Google Scholar