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Abstract
Driven by the idea to use alarm data to explore machine learning across Industry 4.0 applications, the goal of this pilot study was to explore how to collect, store, manage and share data from the ESS Control System. Generally, we seek to make any control system data available for research and innovation but started with alarms as a feasible domain in which to explore machine learning. The goals were threefold, each explored in a work package:
1. How to govern a data ecosystem, and which tools are needed to support it?
2. How can alarm data be interpreted across industrial contexts, i.e., which meta data
and reference models are needed?
3. How can data sharing be practically and legally handled at ESS?
In summary, we identify a set of potential alleys for continued work to foster industrial innovation and collaboration in a control system data ecosystem with ESS as a catalyst.
1. How to govern a data ecosystem, and which tools are needed to support it?
2. How can alarm data be interpreted across industrial contexts, i.e., which meta data
and reference models are needed?
3. How can data sharing be practically and legally handled at ESS?
In summary, we identify a set of potential alleys for continued work to foster industrial innovation and collaboration in a control system data ecosystem with ESS as a catalyst.
| Original language | English |
|---|---|
| Publisher | Lunds Universitet/Lunds Tekniska Högskola |
| Number of pages | 2 |
| Publication status | Published - 2021 Feb 19 |
Publication series
| Name | Technical report |
|---|---|
| Publisher | Lund University, department of computer science |
| No. | 105 |
| ISSN (Print) | 1404-1200 |
Subject classification (UKÄ)
- Information Systems
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- 1 Finished
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ESS Data Lab
Runeson, P. (PI)
Swedish Government Agency for Innovation Systems (Vinnova)
2020/02/01 → 2021/01/31
Project: Research