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Hierarchical Support Vector Data Description for Batch Process Monitoring

Author:
Lv, Zhaomin  Yan, Xuefeng  


Journal:
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH


Issue Date:
2016


Abstract(summary):

Batch process monitoring remains a challenging task due to the inherent time varying dynamics. The occurrence of faults results in the variables having three types of variation behavior, that is, univariate variation,without interaction effect, correlated variable variation with interaction effect, or all variable variation. The three behavior situations may change at any time under dynamic scenarios. Considering that an effective batch process monitoring method should dearly identify the complex variation behavior of the variables, a hierarchical support vector data description (HSVDD), which integrates univariate monitoring, subspace monitoring, and whole space monitoring, is proposed to accurately identify the three variation behaviors. First, three-dimensional data are unfolded to batch-wise data with normalization, and the obtained two-dimensional data are separated according to the time axis to obtain time-slice modeling data. Then, the hierarchical structure, which contains the univariate variable monitoring structure layer, the subspace monitoring structure layer, and the whole space monitoring structure layer, is designed for. each time slice. Meanwhile, support vector data description (SVDD) is adopted as the modeling method in each layer. Lastly, the online hierarchical monitoring model that has the same time point as the online data is selected, and the monitoring results of all models in the hierarchical structure are fused using two methods, that is, obtaining the average weight and the max value. A fault diagnosis method based on a hierarchical monitoring structure is designed. A simple numerical example and fed-batch penicillin fermentation process are employed to demonstrate the effectiveness of the HSVDD.


Page:
9205---9214


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