| Autor: |
Zhai, Fen Lou, Gao, Li Xin, Gong, Neng Chun, Xu, Yong Gang, Feng, Ming Shi |
| Zdroj: |
Applied Mechanics and Materials; March 2011, Vol. 52 Issue: 1 p2039-2044, 6p |
| Abstrakt: |
As the energy distribution in each frequency band of rolling bearing acoustic emission (AE) signal is related to its fault type, so we can use the harmonic wavelet packet to decompose the rolling bearing AE signal of different fault into different frequency band, combine energy in each frequency band together to be a feature vector of the Support Vector Machines (SVM), then being applied to identify the fault through SVM. This paper also compared the Harmonic wavelet packet and Daubechies wavelet packet as well as the SVM and neural networks. The experimental result shows that for the fault pattern identification, the method that combines harmonic wavelet packet decomposition and SVM together can be effective. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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