中国化学工程学报（英文版）
中國化學工程學報（英文版）
중국화학공정학보（영문판）
CHINESE JOURNAL OF CHEMICAL ENGINEERING
2014年
6期
657-663
,共7页
nonlinear process%fault detection%kernel partial least squares%statistical local approach
In this paper, an improved nonlinear process fault detection method is proposed based on modified ker-nel partial least squares (KPLS). By integrating the statistical local approach (SLA) into the KPLS framework, two new statistics are established to monitor changes in the underlying model. The new modeling strategy can avoid the Gaussian distribution assumption of KPLS. Besides, advantage of the proposed method is that the kernel latent variables can be obtained directly through the eigen value decomposition instead of the iterative calculation, which can improve the computing speed. The new method is applied to fault detection in the simulation benchmark of the Tennessee Eastman process. The simulation results show superiority on detection sensitivity and accuracy in com-parison to KPLS monitoring.
