Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5004716 | ISA Transactions | 2014 | 12 Pages |
â¢A totally data-driven multiblock PCA is proposed for plant-wide process monitoring.â¢Mutual information is employed to consider both linear and non-linear relations.â¢The requirement of prior process knowledge is relaxed.â¢The local behavior of process can be well reflected.
Multiblock principal component analysis (MBPCA) methods are gaining increasing attentions in monitoring plant-wide processes. Generally, MBPCA assumes that some process knowledge is incorporated for block division; however, process knowledge is not always available. A new totally data-driven MBPCA method, which employs mutual information (MI) to divide the blocks automatically, has been proposed. By constructing sub-blocks using MI, the division not only considers linear correlations between variables, but also takes into account non-linear relations thereby involving more statistical information. The PCA models in sub-blocks reflect more local behaviors of process, and the results in all blocks are combined together by support vector data description. The proposed method is implemented on a numerical process and the Tennessee Eastman process. Monitoring results demonstrate the feasibility and efficiency.