DMME: Data mining methodology for engineering applications - a holistic extension to the CRISP-DM model

被引：106

作者：

Huber, Steffen ^{[1
]}

Wiemer, Hajo ^{[1
]}

Schneider, Dorothea ^{[1
]}

Ihlenfeldt, Steffen ^{[1
]}

机构：

[1] Tech Univ Dresden, Inst Machine Tools & Control Engn, D-01062 Dresden, Germany

来源：

12TH CIRP CONFERENCE ON INTELLIGENT COMPUTATION IN MANUFACTURING ENGINEERING | 2019年 / 79卷

关键词：

Data mining; Machine learning; Manufacturing data management; Data driven process optimisation;

D O I：

10.1016/j.procir.2019.02.106

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The value of data analytics is fundamental in cyber-physical production systems for tasks like optimization and predictive maintenance. The de facto standard for conducting data analytics in industrial applications is the CRISP-DM methodology. However, CRISP-DM does not specify a data acquisition phase within production scenarios. With this work, we present DMME as an extension to the CRISP-DM methodology specifically tailored for engineering applications. It provides a communication and planning foundation for data analytics within the production domain. We show the feasibility of our methodology for engineering applications within a case study in the field of work piece detection. (C) 2019 The Authors. Published by Elsevier B.V.

引用

页码：403 / 408

页数：6

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