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
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.
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;
Conference_Titel :
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-5537-9
DOI :
10.1109/ICCSIT.2010.5564828
