Conceptual framework for manufacturing data preprocessing of diverse input sources

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

doi:10.1109/INDIN41052.2019.8972327

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 proceeding › Conference contribution › Academic › peer-review

11 Citations (Scopus)

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 language	English
Title of host publication	IEEE International Conference on Industrial Informatics (INDIN)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1041-1046
Number of pages	6
ISBN (Electronic)	978-1-7281-2927-3
ISBN (Print)	978-1-7281-2928-0
DOIs	https://doi.org/10.1109/INDIN41052.2019.8972327
Publication status	Published - 2019
Externally published	Yes
Event	17th International Conference on Industrial Informatics, INDIN 2019 - Aalto University, Espoo, Finland Duration: 22 Jul 2019 → 25 Jul 2019 Conference number: 17 https://www.indin2019.org/

Publication series

Name	IEEE International Conference on Industrial Informatics (INDIN)
Publisher	IEEE
Volume	2019
ISSN (Print)	1935-4576
ISSN (Electronic)	2378-363X

Conference

Conference	17th International Conference on Industrial Informatics, INDIN 2019
Abbreviated title	INDIN 2019
Country/Territory	Finland
City	Espoo
Period	22/07/19 → 25/07/19
Internet address	https://www.indin2019.org/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/INDIN41052.2019.8972327

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). IEEE. https://doi.org/10.1109/INDIN41052.2019.8972327

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title = "Conceptual framework for manufacturing data preprocessing of diverse input sources",

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.",

author = "Dominik Flick and Sebastian Gellrich and Marc-Andr{\'e} Filz and Li Ji and Sebastian Thiede and Christoph Herrmann",

year = "2019",

doi = "10.1109/INDIN41052.2019.8972327",

language = "English",

isbn = "978-1-7281-2928-0",

series = "IEEE International Conference on Industrial Informatics (INDIN)",

publisher = "IEEE",

pages = "1041--1046",

booktitle = "IEEE International Conference on Industrial Informatics (INDIN)",

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note = "17th International Conference on Industrial Informatics, INDIN 2019, INDIN 2019 ; Conference date: 22-07-2019 Through 25-07-2019",

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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). IEEE International Conference on Industrial Informatics (INDIN), vol. 2019, IEEE, Piscataway, NJ, pp. 1041-1046, 17th International Conference on Industrial Informatics, INDIN 2019, Espoo, Finland, 22/07/19. https://doi.org/10.1109/INDIN41052.2019.8972327

Conceptual framework for manufacturing data preprocessing of diverse input sources. / Flick, Dominik; Gellrich, Sebastian; Filz, Marc-André et al.
IEEE International Conference on Industrial Informatics (INDIN). Piscataway, NJ: IEEE, 2019. p. 1041-1046 (IEEE International Conference on Industrial Informatics (INDIN); Vol. 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Conceptual framework for manufacturing data preprocessing of diverse input sources

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AU - Herrmann, Christoph

N1 - Conference code: 17

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N2 - 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.

AB - 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.

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DO - 10.1109/INDIN41052.2019.8972327

M3 - Conference contribution

SN - 978-1-7281-2928-0

T3 - IEEE International Conference on Industrial Informatics (INDIN)

SP - 1041

EP - 1046

BT - IEEE International Conference on Industrial Informatics (INDIN)

PB - IEEE

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T2 - 17th International Conference on Industrial Informatics, INDIN 2019

Y2 - 22 July 2019 through 25 July 2019

ER -

Conceptual framework for manufacturing data preprocessing of diverse input sources

Abstract

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