Analyzing Manufacturing Process By Enabling Process Mining on Sensor Data

Dina Bayomie, Kate Revoredo, Stefan Bachhofner, Kabul Kurniawan, Elmar Kiesling, Jan Mendling

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

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 languageEnglish
Title of host publicationPoEM 2022 Workshops and Models at Work
Subtitle of host publicationProceedings of the PoEM 2022 Workshops and Models at Work co-located with Practice of Enterprise Modelling 2022
Place of PublicationAachen
PublisherCEUR Workshop Proceedings
Number of pages12
Publication statusPublished - 2022
Event2022 Practice of Enterprise Modelling Workshops and Models at Work, PoEM-2022-Workshops-Models at Work - London, United Kingdom
Duration: 23 Nov 202225 Nov 2022

Publication series

SeriesCEUR Workshop Proceedings
Volume3298
ISSN1613-0073

Conference

Conference2022 Practice of Enterprise Modelling Workshops and Models at Work, PoEM-2022-Workshops-Models at Work
Country/TerritoryUnited Kingdom
CityLondon
Period23/11/2225/11/22

Bibliographical note

urn:nbn:de:0074-3298-7

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.

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

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