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 fault detection 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 fault detection residual is generated for each time-slice measurement. The M2D-CCA fault detection scheme performs fault detection using the current measurement and the information provided by its previous samples and batches, and therefore exhibits a superior monitoring performance. The M2D-CCA fault detection 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.

中文翻译：

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基于多目标二维 CCA 的连续批处理工业注塑应用监控


连续的批处理通常涉及批内和批与批之间的关联，并且对此类批处理的监控是必要的。本文提出了一种基于多目标二维典型相关分析（M2D-CCA）的故障检测方案，以实现对连续批次过程的高效监控。首先，将三路历史批处理数据展开为两路时间片数据。其次，对于每个时间片测量，从时间和批次方向在当前测量和先前测量之间执行 CCA，其中考虑了批次内和批次间的相关性。为了确定所涉及的测量值并消除不相关变量的影响，进行多目标进化优化，试图最大化保留的典型相关系数并最小化所涉及变量的数量。最后，基于建立的M2D-CCA模型，为每个时间片测量生成最优的故障检测残差。 M2D-CCA故障检测方案利用当前测量值以及先前样本和批次提供的信息来执行故障检测，因此表现出优越的监控性能。 M2D-CCA 故障检测方法在数值示例和工业注塑过程中进行了测试。监测结果验证了所提出监测方案的可行性和优越性。