DocumentCode
1832154
Title
Notice of Retraction
Research of integrated fault diagnosis for condenser based on multiple neural networks and D-S evidence theory
Author
Peng Daogang ; Zhang Kai ; Zhang Hao ; Huang Conghua
Author_Institution
Coll. of Electr. Power & Autom. Eng., Shanghai Univ. of Electr. Power, Shanghai, China
Volume
2
fYear
2010
fDate
1-3 Aug. 2010
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.
For the reason that the application of the neural network in the condenser fault diagnosis has some restrictions, the result of the fault diagnosis based on neural network is unsatisfied. On the basis of the fault diagnosis of condenser based on neural network, the thought of data fusion is introduced in this paper and a method of condenser integrated fault diagnosis based on multiple neural networks and D-S evidence theory is proposed. According to BP neural network and CPN network, the respective diagnosis results regarded as the primary evidences of D-S theory evidence in decision layer are obtained first, and then these results are fused by using of the evidence theory to obtain the final diagnosis result. The result of simulation shows that: Comparing with the result from the single network, this method has a smaller error and higher diagnosis reliability.
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.
For the reason that the application of the neural network in the condenser fault diagnosis has some restrictions, the result of the fault diagnosis based on neural network is unsatisfied. On the basis of the fault diagnosis of condenser based on neural network, the thought of data fusion is introduced in this paper and a method of condenser integrated fault diagnosis based on multiple neural networks and D-S evidence theory is proposed. According to BP neural network and CPN network, the respective diagnosis results regarded as the primary evidences of D-S theory evidence in decision layer are obtained first, and then these results are fused by using of the evidence theory to obtain the final diagnosis result. The result of simulation shows that: Comparing with the result from the single network, this method has a smaller error and higher diagnosis reliability.
Keywords
backpropagation; capacitors; fault diagnosis; neural nets; power engineering computing; sensor fusion; set theory; uncertainty handling; D-S evidence theory; back propagation; condenser fault diagnosis; counter propagation network; data fusion; decision layer; multiple neural network; Photonics; Condenser; D-S evidence theory; Integrated fault diagnosis; Neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Mechanical and Electronics Engineering (ICMEE), 2010 2nd International Conference on
Conference_Location
Kyoto
Print_ISBN
978-1-4244-7479-0
Type
conf
DOI
10.1109/ICMEE.2010.5558480
Filename
5558480
Link To Document