For the complex batch process with characteristics of unequal batch data length, a novel data-driven batch process monitoring method is proposed based on mixed data features analysis and multi-way kernel entropy component analysis ( MDFA-MKECA ) in this paper. Combining the mechanistic knowledge, different mixed data features of each batch including statistical and thermodynamics entropy features, are extracted to finish data pre-processing. After that, MKECA is applied to reduce data dimensionality and finally establish a monitoring model. The proposed method is applied to a reheating furnace industry process, and the experimental results demonstrate that the MDFA-MKECA method can reduce the calculated amount and effectively provide on-line monitoring of the batch process.

中文翻译：

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MDFA-MKECA的新方法及其在工业批量过程监控中的应用

针对具有批数据长度不相等的复杂批处理过程，提出了一种基于混合数据特征分析和多向核熵成分分析（MDFA-MKECA）的数据驱动的批处理监视方法。结合机械知识，提取每批的不同混合数据特征（包括统计和热力学熵特征）以完成数据预处理。之后，应用MKECA降低数据维数，最终建立监控模型。将该方法应用于加热炉工业生产过程，实验结果表明，MDFA-MKECA方法可以减少计算量，有效地提供了间歇过程的在线监测。