DocumentCode :
550857
Title :
Fault diagnosis approach based on approximate entropy feature extraction with EMD and support vector machines
Author :
Guo Xiaohui ; Ma Xiaoping
Author_Institution :
Sch. of Comput. Sci. & Technol., Xznu Normal Univ., Xuzhou, China
fYear :
2011
fDate :
22-24 July 2011
Firstpage :
4275
Lastpage :
4279
Abstract :
A fault diagnosis approach based on approximate entropy feature selection with empirical mode decomposition and support vector machines is proposed. Firstly, the EMD method is used to decompose the vibration signals into a number of intrinsic mode functions. Secondly, approximate entropy of these intrinsic mode functions which contains main fault information are computed and obtained. Finally, the approximate entropy serve as feature vectors to be input to the multi-class support vector machines and the work conditions and fault patterns are identified by the output of the classifier. This fault diagnosis method is used to recognize the three common faults of ball bearings. The results show that this approach can effectively classify the working conditions and fault patterns of ball bearings accurately even when the number of samples is small.
Keywords :
approximation theory; ball bearings; fault location; feature extraction; mechanical engineering computing; pattern classification; signal processing; support vector machines; vibrations; EMD method; approximate entropy feature extraction; approximate entropy feature selection; ball bearings; classifier; empirical mode decomposition; fault diagnosis approach; fault patterns; fault recognition; feature vectors; intrinsic mode functions; multiclass support vector machine; vibration signal decomposition; Ball bearings; Electronic mail; Entropy; Fault diagnosis; Feature extraction; Manganese; Support vector machines; Approximate Entropy; Empirical Mode Decomposition; Fault Diagnosis; Support Vector Machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2011 30th Chinese
Conference_Location :
Yantai
ISSN :
1934-1768
Print_ISBN :
978-1-4577-0677-6
Electronic_ISBN :
1934-1768
Type :
conf
Filename :
6001197
Link To Document :
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