Fault feature extraction by using adaptive chirplet transform

被引：0
|
作者：
Guo, Qianjin [1 ]
Yu, Haibin [1 ]
Hu, Jingtao [1 ]
机构：
[1] Chinese Acad Sci, Grad Sch, Dept Control Engn & Commun Syst, Beijing 100039, Peoples R China
来源：
WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS | 2006年
关键词：
adaptive chirplet transform; fault detection; feature extraction; time-frequency analysis;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The vibration generated by industrial machines always contains nonlinear and non-stationary signals. It is expected that a desired time-frequency analysis method should have good computation efficiency, and have good resolution in both time domain and frequency domain. In this paper, the adaptive Gaussian chirplet distribution for an integrated time-frequency signature extraction of the machine vibration is presented. The adaptive Gaussian chirplet spectrogram is non-negative, has a high time-frequency resolution, and is free of cross term interference, so it offers the advantage of good localization of the vibration signal energy in the time-frequency domain. Experimental results show that the proposed method is very effective.
引用
收藏
页码：5643 / 5647
页数：5
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