Fault detection based on canonical correlation analysis (CCA) has received increased
attention due to its efficiency in exploring the relationship between input and output …

In intelligent process monitoring and fault detection of the modern process industry,
conventional methods mostly consider singular characteristics of systems. To tackle the …

Canonical correlation analysis (CCA) could be utilized for analyzing a linear static process
when the input-output relationship is explicitly existing. Based on the canonical variates …

This chapter focuses on the application of the canonical correlation analysis (CCA)
technique in dynamic process fault diagnosis. CCA is a typical multivariate analysis tool that …

A locally weighted canonical correlation analysis (LWCCA) method is proposed to achieve
efficient nonlinear process monitoring. The basic idea of the LWCCA is to approximate a …

Recent research has emphasized the successful application of canonical correlation
analysis (CCA) to perform fault detection (FD) in both static and dynamic processes with …

In this article, a just-in-time-learning (JITL)-aided canonical correlation analysis (CCA) is
proposed for the monitoring and fault detection of multimode processes. A canonical …

In the field of multivariate statistical process monitoring (MSPM), fault isolation has attracted
increasing attention, due to its importance in ensuring process reliability and product quality …

Abstract A novel Dynamic Kernel PCA (DKPCA) method is developed for process monitoring
in nonlinear dynamical systems. Classical DKPCA approaches still exhibit vague linearity …

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 …