Title :
Fault Feature Extraction by Using Adaptive Chirplet Transform
Author :
Guo, Qianjin ; Yu, Haibin ; Hu, Jingtao
Author_Institution :
Dept. of Control Eng. & Commun. Syst., Graduate Sch. of Chinese Acad. of Sci., Beijing
Abstract :
The vibration generated by industrial machines always contains nonlinear and nonstationary 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 nonnegative, 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
Keywords :
Gaussian distribution; fault diagnosis; time-frequency analysis; transforms; vibration control; adaptive Gaussian chirplet distribution; adaptive Gaussian chirplet spectrogram; adaptive chirplet transform; cross term interference; fault feature extraction; machine vibration signal energy; nonlinear signal; nonstationary signal; time-frequency analysis; time-frequency resolution; time-frequency signature extraction; Chirp; Energy resolution; Feature extraction; Frequency domain analysis; Interference; Signal generators; Signal resolution; Spectrogram; Time domain analysis; Time frequency analysis; Adaptive chirplet transform; Fault detection; Feature Extraction; Time-frequency analysis;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1714155
