DocumentCode :
3695489
Title :
Fault severity recognition of hydraulic piston pumps based on EMD and feature energy entropy
Author :
Chuanqi Lu;Shaoping Wang;Mileta Tomovic
Author_Institution :
School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
fYear :
2015
fDate :
6/1/2015 12:00:00 AM
Firstpage :
489
Lastpage :
494
Abstract :
Based on Empirical Mode Decomposition (EMD) and feature energy entropy, a method for the fault severity recognition of piston pumps is proposed in this paper. The discharge pressure signals of piston pumps are decomposed into a series of Intrinsic Mode Function (IMF) components by using EMD. Then, some useful IMF components are selected by calculating correlation coefficient between the signal reconstructed by the selected IMFs and the original signal. The characteristic vector is constructed by computing the normalized energy of every selected IMF, and the feature energy entropy can be obtained. The experimental results indicate that the proposed method can recognize the fault severity of pumps effectively.
Keywords :
"Pistons","Footwear","Entropy","Discharges (electric)","Pumps","Wavelet analysis","Correlation coefficient"
Publisher :
ieee
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2015 IEEE 10th Conference on
Type :
conf
DOI :
10.1109/ICIEA.2015.7334162
Filename :
7334162
Link To Document :
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