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

作者：Jiang, Qingchao; Yan, Xuefeng^*

来源：Chemometrics and Intelligent Laboratory Systems, 2014, 136: 121-137.

DOI：10.1016/j.chemolab.2014.05.012

• Multi-mode plant-wide process
• Multi-block principal component analysis
• Joint probability
• Bayesian inference
• Mutual information

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 T-2 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.

• 出版日期2014-8-15
• 单位华东理工大学