Successive batch processes generally involve within-batch and batch-to-batch correlations, and monitoring of such batch processes is imperative. This paper proposes a multiobjective two-dimensional canonical correlati...
详细信息
Successive batch processes generally involve within-batch and batch-to-batch correlations, and monitoring of such batch processes is imperative. This paper proposes a multiobjective two-dimensional canonical correlation analysis (M2D-CCA)-based faultdetection scheme to achieve efficient monitoring of successive batch processes. First, three-way historical batch process data are unfolded into two-way time-slice data. Second, for each time-slice measurement, CCA is performed between the current measurement and previous measurements from both time and batch directions, which takes the within-batch and batch-to-batch correlations into account. To determine the involved measurements and eliminate the influence of unrelated variables, multiobjective evolutionary optimization is performed, which tries to maximize the preserved canonical correlation coefficients and minimize the number of involved variables. Finally, based on the established M2D-CCA model, an optimal faultdetection residual is generated for each time-slice measurement. The M2D-CCA faultdetection scheme performs faultdetection using the current measurement and the information provided by its previous samples and batches, and therefore exhibits a superior monitoring performance. The M2D-CCA faultdetection approach is tested on a numerical example and an industrial injection molding process. Monitoring results verify the feasibility and superiority of the proposed monitoring scheme.
In this paper, two common statistics, the T-2 and the Q statistics, for faultdetection and process monitoring are compared. Specifically, the geometric relationship between the T-2 statistic and 3 common forms of the...
详细信息
In this paper, two common statistics, the T-2 and the Q statistics, for faultdetection and process monitoring are compared. Specifically, the geometric relationship between the T-2 statistic and 3 common forms of the Q statistics is analysed. Furthermore, using the false alarm rate (FAR) and the faultdetection rate (FDR), the faultdetection performance of both statistics is quantified and compared. The results show that, for a given significance level, the T-2 statistic has the best overall FDR. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
暂无评论