DocumentCode :
3287209
Title :
Gear Fault Detection Based on Ensemble Empirical Mode Decomposition and Hilbert-Huang Transform
Author :
Ai, Shufeng ; Li, Hui
Author_Institution :
Dept. of Commun. Technol., Zhejiang Inst. of Media & Commun., Hangzhou
Volume :
3
fYear :
2008
fDate :
18-20 Oct. 2008
Firstpage :
173
Lastpage :
177
Abstract :
A new approach to fault diagnosis of gear crack based on ensemble empirical mode decomposition (EEMD) and Hilbert-Huang transform (HHT) technique is presented. Firstly, the time-domain vibration signal of the gearbox with gear crack fault is measured. Then the original vibration signal is separated into intrinsic oscillation modes, using the ensemble empirical mode decomposition. Secondly, Hilbert transform tracks the modulation energy of the intrinsic mode functions (IMFs) and estimates the instantaneous amplitude and instantaneous frequency. Then the HHT spectrum of the vibration signal can be obtained. Therefore, the character of the gear crack faults can be recognized according to the HHT spectrum. The experimental results show that EEMD and HHT spectrum can effectively diagnose the faults of the gear crack.
Keywords :
Hilbert transforms; crack detection; fault diagnosis; gears; signal processing; vibrations; Hilbert-Huang transform; ensemble empirical mode decomposition; gear crack fault; gear fault detection; gearbox; instantaneous amplitude; intrinsic mode functions; intrinsic oscillation modes; time-domain vibration signal; Fault detection; Fault diagnosis; Frequency estimation; Gears; Signal analysis; Signal resolution; Time frequency analysis; Vibration measurement; Wavelet analysis; Wavelet transforms; Ensemble Empirical Mode Decomposition; Hilbert-Huang transform; faul diagnosis; gear; vibration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on
Conference_Location :
Shandong
Print_ISBN :
978-0-7695-3305-6
Type :
conf
DOI :
10.1109/FSKD.2008.64
Filename :
4666235
Link To Document :
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