DMME: Data mining methodology for engineering applications - A holistic extension to the CRISP-DM model
Research output: Contribution to journal › Conference article › Contributed › peer-review
Contributors
Abstract
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.
Details
Original language | English |
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Pages (from-to) | 403-408 |
Number of pages | 6 |
Journal | Procedia CIRP |
Volume | 79 |
Publication status | Published - 2019 |
Peer-reviewed | Yes |
Conference
Title | 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2018 |
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Duration | 18 - 20 July 2018 |
City | Naples |
Country | Italy |
External IDs
ORCID | /0000-0001-7540-4235/work/161408745 |
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Keywords
ASJC Scopus subject areas
Keywords
- Data driven process optimisation, Data mining, Machine learning, Manufacturing data management