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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Multiobjective Two-Dimensional CCA-Based Monitoring for Successive Batch Processes With Industrial Injection Molding Application
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Multiobjective Two-Dimensional CCA-Based Monitoring for Successive Batch Processes With Industrial Injection Molding Application

机译：基于多目标二维CCA的工业注塑应用连续批生产过程监控

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摘要

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.
机译：连续的批处理通常涉及批内关联和批间关联，因此必须监视此类批处理。本文提出了一种基于多目标二维规范相关分析（M2D-CCA）的故障检测方案，以实现对连续批生产过程的有效监控。首先，将三路历史批处理数据展开为两路时间切片数据。其次，对于每个时间片测量，都在时间和批次方向上在当前测量和先前测量之间执行CCA，这考虑了批次内和批次间的相关性。为了确定所涉及的度量并消除无关变量的影响，执行了多目标进化优化，该尝试试图最大化保留的规范相关系数，并尽量减少所涉及变量的数量。最后，基于已建立的M2D-CCA模型，为每个时间片测量生成最佳故障检测残差。 M2D-CCA故障检测方案使用当前测量值及其以前的样品和批次提供的信息执行故障检测，因此具有出色的监视性能。 M2D-CCA故障检测方法已通过数值示例和工业注塑工艺进行了测试。监测结果验证了所提出监测方案的可行性和优越性。

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