Conceptual framework for manufacturing data preprocessing of diverse input sources

Dominik Flick, Sebastian Gellrich, Marc-André Filz, Li Ji, Sebastian Thiede, Christoph Herrmann

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

The manufacturing industry today is experiencing a never seen increase in available data. These data compromise a variety of different formats, semantics, and quality. It is often distributed in different data sources, e.g. sensor data from the production line, environmental data or machine tool parameters. Coming from the field of application the paper will discuss, within a conceptual framework, the possibilities of how to integrate the diverse existing data-sources and how to pre-process the data with high quality using advanced outlier detection algorithms and developing reasonable outlier treatment values by applying machine-learning methods. The result will be validated with real manufacturing data from an automotive use-case.
Original languageEnglish
Title of host publicationIEEE International Conference on Industrial Informatics (INDIN)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1041-1046
Number of pages6
ISBN (Electronic)978-1-7281-2927-3
ISBN (Print)978-1-7281-2928-0
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event17th International Conference on Industrial Informatics, INDIN 2019 - Aalto University, Espoo, Finland
Duration: 22 Jul 201925 Jul 2019
Conference number: 17
https://www.indin2019.org/

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN)
PublisherIEEE
Volume2019
ISSN (Print)1935-4576
ISSN (Electronic)2378-363X

Conference

Conference17th International Conference on Industrial Informatics, INDIN 2019
Abbreviated titleINDIN 2019
CountryFinland
CityEspoo
Period22/07/1925/07/19
Internet address

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  • Cite this

    Flick, D., Gellrich, S., Filz, M-A., Ji, L., Thiede, S., & Herrmann, C. (2019). Conceptual framework for manufacturing data preprocessing of diverse input sources. In IEEE International Conference on Industrial Informatics (INDIN) (pp. 1041-1046). (IEEE International Conference on Industrial Informatics (INDIN); Vol. 2019). Piscataway, NJ: IEEE. https://doi.org/10.1109/INDIN41052.2019.8972327