Fault detection for chemical process based on improved MSPCA

Abstract

An improved multi-scale principal component analysis (MSPCA) is used for fault detection and diagnosis. Improved MSPCA simultaneously extracts both, cross correlation across the variable (principal component analysis (PCA) approach) and auto-correlation within a variable (wavelet approach). The data collected from the industry condition are processed by means of the nonlinear wavelet threshold denoising method. Using wavelets, the individual variable are decomposed into approximations and details at different scales. Contributions from each scale are collected in separate matrices, and a PCA model is then constructed to extract correlation at each scale. According to the analysis of simulation of chemical process, and comparing the improved MSPCA with MSPCA, it shows that the improved MSPCA has enhanced the accuracy of fault detection in process monitoring.