Fault detection of uncertain nonlinear process using interval-valued data-driven approach

This paper introduces a new structure kernel principal component analysis (KPCA) that can successfully model symbolic interval-valued data for fault detection. In the proposed structure, interval KPCA (IKPCA) method is proposed to deal with interval-valued data. Two IKPCA models are proposed. The first model is based on the centers and ranges of intervals IKPCA(CR) and the second model is based the upper and lower bounds of intervals IKPCA(UL). Residuals are generated and fault detection indices are computed. The aim of using IKPCA is to ensure robustness to false alarm without affecting the fault detection performance. The proposed fault detection approach is carried out using simulation example and Tennessee Eastman Process (TEP). The obtained results demonstrate the effectiveness of the proposed technique. (C) 2019 Elsevier Ltd. All rights reserved.