Abstract
Typical manufacturing processes involve various machines, each of which may be equipped with a variety of sensors. Digital twins can be used to model how the machines operate and support analysts in issue identification and identifying potential improvements in the process. For a complete view of the status of a machine, however, models need to be enriched to identify patterns over changes in the measurements of sensors and correlations between these sensors. Process mining techniques could be usefully applied in this context, given that they provide descriptive analyses to explain and simulate physical objects based on event logs storing multi-perspective data about the process. However, although sensors generate a vast amount of data about the status of machines on the production floor, they cannot be directly used by process mining techniques. To tackle this issue, we introduce a method that creates a custom event log from sensor data based on the process analysts interests. To this end, we propose different encodings for the sensor data. An exploratory experiment using real-life data from an industrial partner shows the effectiveness of our approach.
Original language | English |
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Title of host publication | PoEM 2022 Workshops and Models at Work |
Subtitle of host publication | Proceedings of the PoEM 2022 Workshops and Models at Work co-located with Practice of Enterprise Modelling 2022 |
Place of Publication | Aachen |
Publisher | CEUR Workshop Proceedings |
Number of pages | 12 |
Publication status | Published - 2022 |
Event | 2022 Practice of Enterprise Modelling Workshops and Models at Work, PoEM-2022-Workshops-Models at Work - London, United Kingdom Duration: 23 Nov 2022 → 25 Nov 2022 |
Publication series
Series | CEUR Workshop Proceedings |
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Volume | 3298 |
ISSN | 1613-0073 |
Conference
Conference | 2022 Practice of Enterprise Modelling Workshops and Models at Work, PoEM-2022-Workshops-Models at Work |
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Country/Territory | United Kingdom |
City | London |
Period | 23/11/22 → 25/11/22 |
Bibliographical note
urn:nbn:de:0074-3298-7Funding Information:
This work received funding from the Teaming.AI project in the European Union’s Horizon 2020 research and innovation program under grant agreement No 95740. The work of J. Mendling was supported by the Einstein Foundation Berlin.
Funding Information:
This work received funding from the Teaming.AI project in the European Union's Horizon 2020 research and innovation program under grant agreement No 95740. The work of J. Mendling was supported by the Einstein Foundation Berlin.
Publisher Copyright:
© 2022 Copyright for this paper by its authors.
Keywords
- Event log creation
- Process mining
- Sensor data