Joint Probability Density and Weighted Probabilistic PCA Based on Coefficient of Variation for Multimode Process Monitoring

Abstract

For probabilistic monitoring of multimode processes, this paper introduced a monitoring scheme that integrates joint probability density and weighted probabilistic principal component analysis based on coefficient of variation (CV-WPPCA). A joint probability based on ð‘‡2 statistic was constructed for mode identification. After it concentrated maximum fault-relevant information into dominant subspace by identifying and extracting important noise factors from the residual subspace, the new approach utilized a weighting strategy based on coefficient of variation method to highlight the useful information in the reconstructed dominant subspace. A case study on the Tennessee Eastman process was applied to demonstrate the efficiency of the proposed method.