Improvement of kernel principal component analysis-based approach for nonlinear process monitoring by data set size reduction using class interval

Abstract

Fault detection and diagnosis (FDD) systems play a crucial role in maintaining the adequate execution of the monitored process. One of the widely used data-driven FDD methods is the Principal Component Analysis (PCA). Unfortunately, PCA's reliability drops when data has nonlinear characteristics as industrial processes. Kernel Principal Component Analysis (KPCA) is an alternative PCA technique that is used to deal with a similar data set. For a large-sized data set, KPCA's execution time and occupied storage space will increase drastically and the monitoring performance can also be affected in this case. So, the Reduced KPCA (RKPCA) was introduced with the aim of reducing the size of a given training data set to lower the execution time and occupied storage space while maintaining KPCA's monitoring performance for nonlinear systems. Generally, RKPCA reduces the number of samples in the training data set and then builds the KPCA model based on this data set. In this paper, the proposed algorithm selects relevant observations from the original data set by utilizing a class interval technique (i.e. histogram) to maintain a bunch of representative samples from each bin. The proposed algorithm has been tested on three tank system pilot plant and Ain El Kebira Cement rotary kiln process. The proposed algorithm has successfully maintained homogeneity to the original data set, reduced the execution time and occupied storage space, and led to decent monitoring performance.