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Causal analysis based on non-time-series kernel Granger causality in a steelmaking process

机译：基于非时间序列核Granger因果关系的炼钢过程因果分析

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摘要

In the manufacturing industry, it is extremely important to identify variables that affect product quality. Identifying variables which affect quality variables is called causal analysis. In batch processes, time-series data of process variables and the corresponding data of quality variables are generally acquired. Since causal analysis using the raw data needs a large computation load, it is often performed after compressing time-series process variables data into non-time-series feature variables data. Various causal analysis methods using such data have been developed, however, none have shown effective results in actual plants. In the present work, non-time-series kernel Granger causality (NTS-KGC) is proposed for causal analysis with non-time-series data of batch processes. This is a method that kernel Granger causality [1], which is used for causal analysis with time-series data in nonlinear systems, is expanded for causal analysis with non-time-series data. The validity of the proposed method is demonstrated through a numerical example of a nonlinear batch process. In addition, we conducted a case study of applying NTS-KGC to data obtained from a real steelmaking process. The results demonstrate that NTS-KGC is superior to other existing methods using the following indexes, i.e. variable influence on projection (VIP) of partial least squares (PLS), regression coefficients of PLS, and variable importance of Random Forest.
机译：在制造业中，识别影响产品质量的变量非常重要。识别影响质量变量的变量称为因果分析。在批处理中，通常获取过程变量的时间序列数据和相应的质量变量数据。由于使用原始数据进行因果分析需要很大的计算量，因此通常在将时间序列过程变量数据压缩为非时间序列特征变量数据之后执行。已经开发了使用这些数据的各种因果分析方法，但是，没有一种方法在实际工厂中显示出有效的结果。在当前工作中，提出了非时间序列内核Granger因果关系（NTS-KGC）用于批处理过程的非时间序列数据的因果分析。这是一种用于扩展非线性系统中用于按时间序列进行因果分析的内核Granger因果关系[1]的方法。通过非线性批处理过程的数值例子证明了该方法的有效性。此外，我们进行了将NTS-KGC应用于从实际炼钢过程中获得的数据的案例研究。结果表明，NTS-KGC优于使用以下指标的其他现有方法，即对偏最小二乘（PLS）的投影（VIP）的变量影响，PLS的回归系数以及随机森林的变量重要性。

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