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.

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Considering that the deviation between normal and abnormal status captured by each independent component (IC) is different from each other, a statistical analysis-driven approach by integrating kernel density estimation (KDE) with weighted independent component analysis (KDE-WICA) is developed. In KDE-WICA, KDE is used to estimate the probability and evaluate the importance of each IC, and subsequently set different weighting values on the ICs to highlight the deviation information for process monitoring. To overcome drastic fluctuations in the monitoring result, given that the previous status is not considered in determining the current status, a statistical weighting strategy is proposed to comprehensively evaluate the status of the process within a moving window (KDE-DWICA) and further improve the monitoring performance. KDE-DWICA is exemplified using a numerical study and the Tennessee–Eastman benchmark process. The monitoring results indicate that the performance of KDE-DWICA is superior to those of PCA, ICA, and other state-of-the-art variant-based methods.

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