Learning Causal Graphs in Manufacturing Domains using Structural Equation Models

被引：0

作者：

Kertel, Maximilian ^{[1
]}

Harmeling, Stefan ^{[2
]}

Pauly, Markus ^{[3
]}

机构：

[1] BMW Grp, Technol Dev Battery Cell, Munich, Germany

[2] TU Dortmund Univ, Dept Comp Sci, Dortmund, Germany

[3] TU Dortmund Univ, Dept Stat, Dortmund, Germany

来源：

2022 5TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE FOR INDUSTRIES, AI4I | 2022年

关键词：

Causal Discovery; Bayesian Networks; Industry; 4.0; DISCOVERY;

D O I：

10.1109/AI4I54798.2022.00010

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known they pose a challenge to effective process control. In this work we present how Structural Equation Models can be used for deriving cause-and-effect relationships from the combination of prior knowledge and process data in the manufacturing domain. Compared to existing applications, we do not assume linear relationships leading to more informative results.

引用

页码：14 / 19

页数：6

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