Traditional principal component analysis (PCA) algorithm

targeting to explore correlations among measured variables in training dataset

has been intensively investigated and applied to data-driven fault detection. However

all variables are considered equally important in modeling process of traditional PCA-based methods

the difference between variable correlations cannot be comprehensively described. A variable weighted PCA (VWPCA) algorithm was proposed and applied to fault detection. Weight calculations were performed on the training dataset so correlation differences among measured variables were fully reflected in the processed data and a distributed PCA-based fault detection model was constructed. When implemented in online fault detection

the Bayesian inference was used to combine multiple monitoring results into an ensemble of probability indices. VWPCA approach assigned different weights to different variables according to the correlation difference

thus PCA modeling took correlation difference into account and the models could completely describe characteristics of the training dataset. Finally

superiority of the proposed VWPCA method was validated by well-known TE process.