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《High Voltage Apparatus》 2005-06
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Electric Machine Fault Diagnosing Based on Energy Spectrum of Gauss Chirplet Transforms

HU Guo-sheng, ZHU Feng-feng, REN Zhen, TU Yong-jun (Guangdong Vocational College of Science & Tech., Guangzhou 510640, China; Maths School, South China Univ. of Tech., Guangzhou 510640, China; Electric Power School, South China Univ. of Tech., Guangzhou 510640, China)  
In this paper, a new method of diagnosing electric machine fault, which based on gauss chirplet transforms, is presented. Chirplet transforms (CTs) are variation of the time, the frequency, and the scale of the signal. CTs have more effective than wavelet transforms in time-frequency analyzing of non-stable signals. Energy spectrums of electric machine fault signal can be gauged by using Gauss CTs, so the frequencies of the signal can be extracted. Experiment results show this method is more concentrated in energy than fourier transform and wavelet transform.
【Fund】: 国家自然科学基金资助(50077008) 广东省自然科学基金资助(033044)
【CateGory Index】: TM307.1
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