Title :
Notice of Retraction
FastICA-SVM fault diagnosis for batch process
Author :
Qing Yang ; Jingran Guo ; Xu Zhang
Author_Institution :
Sch. of Inf. Sci. & Eng., Shenyang Ligong Univ., Shenyang, 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.
An ensemble fault diagnosis approach based on fast independent component analysis and support vector machine (FastICA-SVM) for non-Gaussian complex process is presented. Firstly fast independent component analysis is used as a feature extraction step, and then classifier is constructed by SVM for fault diagnosis. The experimental results of benchmark of the fed-batch penicillin fermentation process indicate that FastICA-SVM method can diagnosis faults more efficient and has better performance than the SVM method.
Keywords :
batch processing (industrial); fault diagnosis; feature extraction; independent component analysis; production engineering computing; support vector machines; FastICA-SVM fault diagnosis; batch process; ensemble fault diagnosis approach; fast independent component analysis; feature extraction; nonGaussian complex process; support vector machine; Batch production systems; Classification algorithms; Fault diagnosis; Independent component analysis; Kernel; Monitoring; Support vector machines; FastICA-SVM; batch process; fast independent component analysis; fault diagnosis; support vector machine;
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6022276
