DocumentCode
1958389
Title
Notice of Retraction
Application of EEMD in fault diagnosis of diesel engine
Author
Shiding Luo ; Lingling Zhang ; Yiguan Zhao ; Yunkui Xiao ; ShengLin Fang
Author_Institution
Automobile Eng. Dept., Acad. of Mil. Transp., Tianjin, China
Volume
1
fYear
2010
fDate
9-11 July 2010
Firstpage
465
Lastpage
468
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The principle and algorithm of Empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) is introduced, and two methods are respectively used to analyze simulated signal and engine´s vibration signals. Based on EEMD, the signal can be efficiently decomposed into a finite number of intrinsic mode functions (IMFs) by adding white noise, and the problem of mode mixing in frequency which is drawback of EMD is avoided. Experimental results show that EEMD has obvious superiority to EMD on processing vibration signal, and the method may be applied to fault diagnosis of diesel engine by efficiently extracting fault feature in the marginal Hilbert spectrum.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The principle and algorithm of Empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) is introduced, and two methods are respectively used to analyze simulated signal and engine´s vibration signals. Based on EEMD, the signal can be efficiently decomposed into a finite number of intrinsic mode functions (IMFs) by adding white noise, and the problem of mode mixing in frequency which is drawback of EMD is avoided. Experimental results show that EEMD has obvious superiority to EMD on processing vibration signal, and the method may be applied to fault diagnosis of diesel engine by efficiently extracting fault feature in the marginal Hilbert spectrum.
Keywords
acoustic signal processing; diesel engines; fault diagnosis; feature extraction; vibrations; white noise; diesel engine; ensemble empirical mode decomposition; fault diagnosis; fault feature extraction; intrinsic mode function; marginal Hilbert spectrum; vibration signal processing; white noise; Algorithm design and analysis; Engines; empirical mode decomposition; ensemble empirical mode decomposition; fault diagnosis; marginal hilbert spectrum;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5565053
Filename
5565053
Link To Document