DocumentCode
1953781
Title
Notice of Retraction
The diagnosis of tool wear based on RBF neural networks and D-S evidence theory
Author
Weiqing Cao ; Pan Fu ; Weilin Li
Author_Institution
Sch. Of Mech. Eng., Southwest Jiaotong Univ., Chengdu, China
Volume
7
fYear
2010
fDate
9-11 July 2010
Firstpage
409
Lastpage
411
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.
In view of uncertain factors in the machining process, the paper puts forward a two-level information fusion method based on RBF neural network and D-S evidence theory. Three different signals were used to train and test three RBF neural networks and the outputs of three RBF networks were aggregated using the D-S evidence theory. Experiments show that the combination of RBF neural network and D-S evidence theory can improve the efficiency and accuracy of the tool wear fault 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.
In view of uncertain factors in the machining process, the paper puts forward a two-level information fusion method based on RBF neural network and D-S evidence theory. Three different signals were used to train and test three RBF neural networks and the outputs of three RBF networks were aggregated using the D-S evidence theory. Experiments show that the combination of RBF neural network and D-S evidence theory can improve the efficiency and accuracy of the tool wear fault diagnosis.
Keywords
fault diagnosis; inference mechanisms; machine tools; mechanical engineering computing; production engineering computing; radial basis function networks; sensor fusion; uncertainty handling; wear; D-S evidence theory; RBF neural networks; machining process; tool wear fault diagnosis; two-level information fusion method; Reliability theory; Space charge; D-S evidence theory; RBF neural network; wear diagnosis;
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.2010.5564828
Filename
5564828
Link To Document