Nonlinear plant-wide process monitoring using MI-spectral clustering and Bayesian inference-based multiblock KPCA

• A novel totally data-driven multiblock process monitoring method is proposed.
• Mutual information-spectral clustering is proposed for block division.
• Both linear and nonlinear relations are considered in block division.
• KPCA is employed to model the nonlinear relations in each block.
• Results in all blocks are combined together by Bayesian inference.

Multiblock or distributed strategies are generally used for plant-wide process monitoring, and the blocks are usually obtained based on prior process knowledge. However, process knowledge is not always available in practical application. This work aims to develop a totally data-driven distributed method for nonlinear plant-wide process monitoring. By performing mutual information-spectral clustering, the measured variables are automatically divided into sub-blocks that account for both linear and nonlinear relations among variables. Considering that the variables in the same sub-block can be nonlinearly related, kernel principal component analysis (KPCA) monitoring model is established in each sub-block. The sub-KPCA models reflect more local behaviors of a process, and the monitoring results of all blocks are combined together by Bayesian inference to provide an intuitionistic indication. The efficiency of the proposed method is demonstrated using a numerical example and the Tennessee Eastman benchmark process.