Abstract
The manufacturing industry and, for this research, the automotive manufacturing industry specifically, is always on the lookout for opportunities to improve production throughput with a minimum of investment. Identifying these opportunities often requires the observation of the current production process by experts. This paper is the continuation of the previous work ’Automated, Nomenclature Based Data Point Selection for Industrial Event Log Generation’. One of its aims is to provide strategies that can be used to pre-process an in-depth, slightly flawed industrial equipment log to allow for further analysis. The pre-processing is achieved by identifying the flaws, removing the non-value added events and a heuristic methodology to cluster the log into individual sequences. Expert knowledge then is encoded into engineering features to extend the log matrix and prepare it for machine learning model generation for identification of the complete cases. To derive value from the available data, the sequences are plotted into Gantt charts, and eight hypotheses are introduced that allow for automated annotations within this chart to highlight potential areas of improvement. Application of the framework to real life logs, obtained from stations considered bottlenecks within the evaluated automotive body shop, lead to the discovery of improvement potential between two and twelve seconds per cycle.
| Original language | English |
|---|---|
| Title of host publication | Automatic Generation of Improvement Suggestions for Legacy, PLC Controlled Manufacturing Equipment Utilizing Machine Learning |
| Editors | Jürgen Beyerer, Alexander Maier, Oliver Niggemann |
| Publisher | Springer Verlag |
| Pages | 93-102 |
| Number of pages | 10 |
| ISBN (Electronic) | 978-3-662-62746-4 |
| ISBN (Print) | 978-3-662-62745-7 |
| DOIs | |
| Publication status | E-pub ahead of print - 24 Dec 2020 |
| Event | 5th International conference Machine Learning for Cyber Physical Systems - Berlin, Germany Duration: 12 Mar 2020 → 13 Mar 2020 |
Conference
| Conference | 5th International conference Machine Learning for Cyber Physical Systems |
|---|---|
| Abbreviated title | ML4CPS |
| Country/Territory | Germany |
| City | Berlin |
| Period | 12/03/20 → 13/03/20 |
Bibliographical note
This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Keywords
- Industrial Logs
- Process Mining
- Case Clustering