Static and dynamic rubbing positions identification of Cryocooler based on wavelet packet analysis and support vector machine
Received:January 29, 2019  Revised:July 09, 2019  download
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Author NameAffiliationE-mail
GAO Sheng Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai 200083,China
University of the Chinese Academy of Sciences,Beijing 100049,China 
gaosheng@mail.ustc.edu.cn 
WU Yi-Nong Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai 200083,China wyn@mail.sitp.ac.cn 
JIANG Zhen-Hua Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai 200083,China jiangzhenhua@mail.sitp.ac.cn 
Abstract:Cryocooler plays an extremely important role in the field of infrared remote sensing. The normal operation and performance of the detector will be affected if the cryocooler breaks down. A new intelligent fault diagnosis method for cryocooler has been proposed based on wavelet packet transform, genetic algorithm and SVM for rubbing fault. First, wavelet transform is applied to the vibration signal, and the vibration signal is extracted in time domain. The evaluation factors of the combined feature set are calculated by using the distance evaluation technique, and the corresponding sensitive features are selected. Then, the parameters of SVM are optimized by genetic algorithm. Finally, the selected sensitive features are input into the optimized SVM to identify different machine operation states automatically. The effectiveness of the method is verified by the fault simulation test of the cryocooler. Experimental results show that this method can identify and locate the cryocooler rubbing fault accurately, and the accuracy is 95%.
keywords:vibration signal  wavelet packet  support vector machine  genetic algorithm  rubbing fault  Pattern recognition
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