LGSPP-Bayes for Fault Detection and Diagnosis

摘要

It has been proved that global and local structure are both important for process monitoring, but principal component analysis (PCA) and locality preserving projections (LPP) can not consider them simultaneously in the process of dimension reduction. This article proposes a novel method named local and global structure preserving projections with Bayes classification (LGSPP-Bayes). The original data is projected to low dimensional feature space and the data projected matrix from high dimension space to low dimension space is gotten. Bayesian classifier then is designed to detect and diagnose faults. Case studies on TEP illustrate the effectiveness of the proposed method.