Abstract
In this presentation we briefly describe potential benefits of using data analysis methods to improve maintenance processes. After a short introduction to an automated, multi-step maintenance process and a survey of the state of data in industry, we explain, how selected data analysis methods can be used to improve maintenance demand detection
| Original language | English |
|---|---|
| Title of host publication | Data Science - Analytics and Applications |
| Subtitle of host publication | Proceedings of the 1st International Data Science Conference - iDSC2017 |
| Publisher | Springer Vieweg |
| Pages | 105-107 |
| Number of pages | 3 |
| ISBN (Print) | 978-3-658-19286-0 |
| Publication status | Published - Jun 2017 |
| Event | International Data Science Conference - Austria, Salzburg Duration: 12 Jun 2017 → 13 Jun 2017 Conference number: 1 https://idsc.at/ |
Conference
| Conference | International Data Science Conference |
|---|---|
| Abbreviated title | iDSC |
| City | Salzburg |
| Period | 12/06/17 → 13/06/17 |
| Internet address |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- Maintenance
- Process Monitoring
- Data Science
- Unsupervised Learning
Classification according to Österreichische Systematik der Wissenschaftszweige (ÖFOS 2012)
- 202003 Automation
Applied Research Level (ARL)
- ARL Level 2 - Description of the application of a principle
Research focus/foci
- Industrial Informatics
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