Vine Copula-Based Dependence Description for Multivariate Multimode Process Monitoring

Plant-wide process monitoring by using weighted copula-correlation based multiblock principal component analysis approach and online-horizon Bayesian method

Multiblock methods have been proposed to capture the complex characteristics of plant-wide monitoring due to the enlargement of process industries. These methods based on automatic sub-block division and copula-correlation, which simultaneously describe the correlation degree and correlation patterns, are designed for sub-block partition. However, the selection of variables for each sub-block through copula-correlation analysis requires a pre-defined cutoff parameter which is difficult to be determined without sufficient prior knowledge, and a "bad" parameter leads to a degraded performance. Therefore, a weighted copula-correlation-based multiblock principal component analysis (WCMBPCA) is proposed. First, the variables in each sub-block are obtained through the copula-correlation analysis-based weighted strategy rather than the cutoff parameter, which highly avoids information loss and prevents "noisy" information. Second, a PCA model is established in each sub-block. Third, a Bayesian inference strategy is used to merge the monitoring results of all sub-blocks. Finally, an online-horizon Bayesian fault diagnosis system is established to identify the fault type of the system based on the statistics of each sub-block. The average detection rate and the average diagnosis rate for numerical example are 77.85% and 98.95%, and that for TE example are 80.63% and 89.50%. Comparing with other candidate methods, the proposed method achieves excellent detection and diagnostic performance.