A Dynamic Fault Detection Method for Nonlinear Process

Abstract

The data-driven methods based multivariate regression have become popular in the area of fault detection due to the development of the computer technique. However, some traditional data-driven methods only consider the statical operating environment that the dynamic relationship in the variables will be ignored to bring some false detection results. In this study, an approach called the dynamic fault detection (DFD) is proposed to solve dynamic behavior under the nonlinear case. From the view of the best KPIs, the proposed method divides the variables into two orthogonal subspaces by the improved kernel principal component regression to judge whether the happened fault is relevant to KPIs or not. Finally, in the numerical simulation, the effectiveness of the DFD approach is demonstrated by comparing it with three nonlinear methods.