Multivariate statistical process monitoring (MSPM) can conduct dimensionality reduction on process variables and can obtain low-dimensional representations that capture most of the information in the original data space. However, most MSPM models are developed under unsupervised situations. Therefore, any abandoned information may deteriorate the process monitoring performance. To address both issues (i.e., dimension reduction and information preservation), this paper proposes a distributed statistical process monitoring scheme. The proposed method employs principal component analysis to derive four distinct and explicable subspaces from the original process variables according to their relevance or irrelevance to principal component subspace and residual subspace. Each subspace serves as a low-dimensional representation of the original data space, thereby preserving the information of the original data space without undergoing information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, was calculated directly in each subspace for fault detection. The Bayesian inference was then introduced as the decision fusion strategy to obtain a final and unique probability index. The feasibility and superiority of the proposed method was investigated by conducting a case study of the well-known Tennessee Eastman process.