Monitoring multi-mode plant-wide processes by using mutual information-based multi-block PCA, joint probability, and Bayesian inference

• Multi-block multimode PCA is proposed for multimode plant-wide process monitoring.
• An MI-based method is proposed to divide the blocks without prior process knowledge.
• Joint probability of blocks is defined to identify the running-on mode.
• Bayesian inference is employed to combine the results in different blocks.
• Weighted contribution plot is proposed to improve fault identification performance.

A multi-mode plant-wide process monitoring scheme that integrates mutual information (MI)-based multi-block principal component analysis (PCA), joint probability, and Bayesian inference is developed. Given that the prior process is not always available, an MI-based block division method is proposed to divide blocks automatically by considering both cross-relations and high-order statistical information among variables. The PCA monitoring model is established in each sub-block and each mode, and a joint probability based on T2 statistics is defined to identify running-on mode. Then, the statistics in different sub-blocks are combined by using Bayesian inference to provide an intuitive indication. Finally, an improved contribution plot method is proposed to identify the root cause of faults. The feasibility and efficiency of the proposed method are evaluated by case studies on a numerical process and the Tennessee Eastman benchmark process. Monitoring results and comparisons with conventional PCA methods indicate the superiority of the proposed method.