Nonlinear Process Monitoring and Fault Diagnosis Based on KPCA and MKL-SVM

A new method for nonlinear process monitoring and fault diagnosis based on kernel principal analysis and multiple kernel learning support vector machines is proposed. The data is analyzed using KPCA. T2 and SPE are constructed in the future space. If the T2 and SPE exceed the predefined control limi...

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Bibliographic Details
Published in2010 International Conference on Artificial Intelligence and Computational Intelligence Vol. 1; pp. 233 - 237
Main Authors Jie Xu, Shousong Hu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2010
Subjects
Fault detection
Fault diagnosis
Kernel
kernel principal component analysis
Machine learning
Monitoring
multiple kernel learning
Principal component analysis
process monitoring
Support vector machines
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Summary:A new method for nonlinear process monitoring and fault diagnosis based on kernel principal analysis and multiple kernel learning support vector machines is proposed. The data is analyzed using KPCA. T2 and SPE are constructed in the future space. If the T2 and SPE exceed the predefined control limit, a fault may have occurred. Then the nonlinear score vectors are calculated and fed into the MKL-SVM to identify the faults. The results of the monitoring application to the Tennessee Eastman (TE) chemical process demonstrate the effectiveness of the proposed method.
ISBN:1424484324
9781424484324
DOI:10.1109/AICI.2010.56