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

TLDR

Data analytics is essential in cyber‑physical production systems, yet the CRISP‑DM standard lacks a data‑acquisition phase for industrial contexts. This work introduces DMME, an extension of CRISP‑DM designed for engineering applications. DMME establishes a communication and planning framework for data analytics in production environments. We demonstrate the feasibility of DMME in a case study on work‑piece detection.

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.