DocumentCode
527458
Title
Notice of Retraction
Fault diagnosis of engine mission using modified Elman neural network
Author
Yu guo Wu ; Chong zhi Song ; Li Ping Shi
Author_Institution
Sch. of Mech. Eng., Anhui Univ. of Technol., Maanshan, China
Volume
2
fYear
2010
fDate
10-12 Aug. 2010
Firstpage
996
Lastpage
998
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.
Based on the fault diagnosis system of engine mission and Elman neural network, it analyses the shortage diagnosis of Elman network, and puts forward the modified Elman network, and applied in fault diagnosis of engine mission. By using conventional “frequency domain” analysis method, modified Elman networks fault diagnosis of engine mission is carried out. It is proved that fault diagnosis of engine mission based on neural networks has upper precision and diagnosed engine mission, improved effectiveness and quality of diagnosis.
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.
Based on the fault diagnosis system of engine mission and Elman neural network, it analyses the shortage diagnosis of Elman network, and puts forward the modified Elman network, and applied in fault diagnosis of engine mission. By using conventional “frequency domain” analysis method, modified Elman networks fault diagnosis of engine mission is carried out. It is proved that fault diagnosis of engine mission based on neural networks has upper precision and diagnosed engine mission, improved effectiveness and quality of diagnosis.
Keywords
engines; fault diagnosis; frequency-domain analysis; mechanical engineering computing; neural nets; Elman neural network; engine mission; fault diagnosis; frequency domain analysis; Artificial neural networks; Educational institutions; Engines; Fault diagnosis; Frequency domain analysis; Gears; Testing; Engine mission; Fault diagnosis; Modified Elman neural network; Neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location
Yantai
Print_ISBN
978-1-4244-5958-2
Type
conf
DOI
10.1109/ICNC.2010.5582900
Filename
5582900
Link To Document