Abstract:
This paper discusses the monitoring of dynamic process. In recent years, Kernel Principal component analysis (KPCA) has gained significant attention as a monitoring metho...Show MoreMetadata
Abstract:
This paper discusses the monitoring of dynamic process. In recent years, Kernel Principal component analysis (KPCA) has gained significant attention as a monitoring method of nonlinear systems. However, the fixed KPCA model limit its application for dynamic systems. For this purpose a new Variable Moving Window Kernel PCA (VMWKPCA) method is introduced to update the KPCA model. The basic idea of this technique is to vary the size of the moving window depending on the normal change of the process. Then the VMWKPCA method is performed for monitoring a Chemical reactor (CSTR). The simulation results proved that the new method is effective.
Date of Conference: 19-21 January 2017
Date Added to IEEE Xplore: 23 October 2017
ISBN Information:
Laboratory of Automatic Signal and Image Processing (LARATSI), University of Monastir, Tunisia
Laboratory of Automatic Signal and Image Processing (LARATSI), University of Monastir, Tunisia
Laboratory of Automatic Signal and Image Processing (LARATSI), University of Monastir, Tunisia
Badji Mokthar, Anaba University, Algeria
Laboratory of Automatic Signal and Image Processing (LARATSI), University of Monastir, Tunisia
Contents Laboratory of Automatic Signal and Image Processing (LARATSI), University of Monastir, Tunisia
Laboratory of Automatic Signal and Image Processing (LARATSI), University of Monastir, Tunisia
Laboratory of Automatic Signal and Image Processing (LARATSI), University of Monastir, Tunisia
Badji Mokthar, Anaba University, Algeria
Laboratory of Automatic Signal and Image Processing (LARATSI), University of Monastir, Tunisia
