DocumentCode
3597318
Title
Notice of Retraction
Diagnosis method for connection-related faults in motion system based on SVM
Author
Jin-Zhuang Xiao ; Hong-Rui Wang ; Zheng Gao
Author_Institution
Coll. of Electr. & Inf. Eng., Hebei Univ., Baoding, China
Volume
2
fYear
2009
Firstpage
1053
Lastpage
1058
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 order to arising the safety and reliability, and monitoring the working states of numerical control system, aiming at the multi-kinds of potential connection-related faults, the construction and the principle of the system were analyzed, and the systemic diagnosis framework was developed. Using the position signal and the torque monitoring one, the parameters of support vector machine were trained where the Gaussian function was employed as nonlinear kernel. The mentioned faults were diagnosed benefiting from the decision function where the parameters were from the trained results. Above method was applied to an X-Y motion platform where data acquisitions, training of support vector machine and fault diagnosis were carried out. The results validate the feasibility of the SVM method.
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 order to arising the safety and reliability, and monitoring the working states of numerical control system, aiming at the multi-kinds of potential connection-related faults, the construction and the principle of the system were analyzed, and the systemic diagnosis framework was developed. Using the position signal and the torque monitoring one, the parameters of support vector machine were trained where the Gaussian function was employed as nonlinear kernel. The mentioned faults were diagnosed benefiting from the decision function where the parameters were from the trained results. Above method was applied to an X-Y motion platform where data acquisitions, training of support vector machine and fault diagnosis were carried out. The results validate the feasibility of the SVM method.
Keywords
fault diagnosis; numerical control; pattern classification; support vector machines; Gaussian function; SVM; connection-related faults; data acquisitions; fault diagnosis; motion system; nonlinear kernel; numerical control system; position signal; support vector machine; support vector machine training; torque monitoring; Computer numerical control; Fault detection; Fault diagnosis; Kernel; Machine learning; Monitoring; Robots; Support vector machine classification; Support vector machines; Torque; Connection-related faults; Gaussian kernel; Numeric control; SVM; X-Y platform;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2009 International Conference on
Print_ISBN
978-1-4244-3702-3
Type
conf
DOI
10.1109/ICMLC.2009.5212383
Filename
5212383
Link To Document