Incipient Fault Detection for Chemical Processes Using Two-Dimensional Weighted SLKPCA

Early detection of incipient faults is a challenging task in chemical process monitoring field. As an effective incipient fault detection tool, statistical local kernel principal component analysis (SLKPCA) has demonstrated its advantage over the traditional kernel principal component analysis (KPCA). However, how to improve its incipient fault detection performance is still a valuable problem. In this paper, an enhanced SLKPCA method, referred to as two-dimensional weighted SLKPCA (TWSLKPCA), is proposed by integrating the sample and component weighting strategies. Different to KPCA, SLKPCA monitors the process changes based on the residual vectors computed by the statistical local approach. To highlight the influence of the faulty residual samples, the residual sample weighting strategy is first designed based on the distance between the tested samples and the training samples, which puts large weights on the samples with strong fault information. Furthermore, the residual component weighting strategy is developed to assign large weights to the sensitive components, which are judged by computing the mutual information between the sample-weighted residual components and the original measured variables. Based on the two-dimensional weighted residual vectors, two monitoring statistics are built to detect the incipient faults. Finally, simulations on a numerical example and the Tennessee Eastman process are used to demonstrate the superiority of the proposed TWSLKPCA method.