Since data collected from chemical processes always have high dimensions

modeling directly can be very complex. PCA can extract main features of the original data and obtain a more compact representation. However

the traditional PCA process monitoring scheme may cause information loss

since it only preserves principal components with large variance

which will greatly affect the performance of process monitoring. To handle this problem

a novel subspace monitoring method based on full variable information was proposed. Firstly

Based on the similarity level between each variable and principal component subspace (PCS) or residual subspace (RS)

the original data space was divided into three low-dimensional subspaces

which preserved the whole process variables. Thus it could use the process information better. Secondly

the monitoring models were established respectively in each subspace

and then the Bayesian inference was introduced to integrate monitoring results of the subspaces. Finally

the feasibility and effectiveness of the FVI method were illustrated through a numerical example and the Tennessee Eastman process.