Dynamic processes monitoring using recursive kernel principal component analysis

The dynamic process monitoring is discussed in this paper. Kernel principal component analysis (KPCA) is a nonlinear monitoring method that cannot be employed for dynamic systems. Recursive KPCA (RKPCA) is proposed to monitor the dynamic processes, which is adaptive monitoring method by computing recursively the eigenvalues and eigenvectors in the kernel space when the training data are updated dynamically. The contributions of this article are as follows: (1) The model of history data is used to build new model after the new sample is obtained. The expensive computation is avoided in this article. (2) New nonlinear modeling method is proposed based on a new singular value decomposition (SVD) technique. The results are interesting due to the nonlinear time evolution of the variables involved. The proposed algorithm was applied to the continuous annealing process and penicillin fermentation process for adaptive monitoring and RKPCA could efficiently capture the time-varying and nonlinear relationship in process variables.

► Modeling method of the nonlinear process is proposed based on a new singular value decomposition (SVD) technique.
► Model of history data is used to build new model after the new sample is obtained.
► New singular value decomposition (SVD) technique is proposed.
► Expensive computation is avoided.