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Ensemble empirical mode decomposition and Hilbert-Huang transform applied to bearing fault diagnosis

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3 Author(s)
Hui Li ; Dept. of Electromech. Eng., Shijiazhuang Inst. of Railway Technol., Shijiazhuang, China ; Yucai Wang ; Yanfang Ma

A signal analysis technique for bearing fault diagnosis based on ensemble empirical mode decomposition (EEMD) and Hilbert-Huang transform (HHT) is presented. EEMD can adaptively decompose vibration signal into a series of zero mean Amplitude Modulation-Frequency Modulation (AM-FM) Intrinsic Mode Functions (IMFs) without mode mixing. Hilbert transform tracks the modulation energy of the interesting Intrinsic Mode Functions (IMFs) and estimates the instantaneous amplitude and instantaneous frequency at any time instant. In the end, the Hilbert-Huang transform spectrum is applied to the vibration signal. Therefore, the character of the bearing fault can be recognized according to the Hilbert-Huang transform spectrum. The experimental results show that Hilbert-Huang transform spectrum analysis based on EEMD and HHT provide a viable signal analysis tool for bearing fault detection.

Published in:

Image and Signal Processing (CISP), 2010 3rd International Congress on  (Volume:7 )

Date of Conference:

16-18 Oct. 2010

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