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Data Acquisition and Preparation - Enabling Data Analytics Projects within Production

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00005915
URN to cite this document: urn:nbn:de:bvb:703-epub-5915-4

Title data

Schock, Christoph ; Dumler, Jonas ; Döpper, Frank:
Data Acquisition and Preparation - Enabling Data Analytics Projects within Production.
In: Procedia CIRP. Vol. 104 (2021) . - pp. 636-640.
ISSN 2212-8271
DOI der Verlagsversion: https://doi.org/10.1016/j.procir.2021.11.107

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Project information

Project financing: Bayerische Forschungsstiftung

Abstract

The increasing amount of available data in production systems is associated with great potential for process optimization. Due to lack of a data analytics methodology and low data quality within production these potentials often remain unused. Therefore, in this paper we present a model for data acquisition and data preparation including feature engineering for characteristic sensor signals of production machines. The model allows the extraction of relevant process information from the signal, which can be used for monitoring, KPI tracking, trend analysis and anomaly detection. The approach is evaluated on an industrial turning process.

Further data

Item Type: Article in a journal
Keywords: Data Analytics; CRISP-DM; Data Acquisition; Data Preparation; Feature Engineering; Process Monitoring; Condition Monitoring
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Institutions of the University: Faculties > Faculty of Engineering Science > Chair Manufacturing and Remanufacturing Technology
Faculties > Faculty of Engineering Science > Chair Manufacturing and Remanufacturing Technology > Chair Manufacturing and Remanufacturing Technology - Univ.-Prof. Dr.-Ing. Frank Döpper
Research Institutions > Affiliated Institutes > Fraunhofer-Projectgroup Processinnovation
Faculties
Faculties > Faculty of Engineering Science
Research Institutions
Research Institutions > Affiliated Institutes
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-5915-4
Date Deposited: 08 Dec 2021 06:58
Last Modified: 08 Dec 2021 09:26
URI: https://epub.uni-bayreuth.de/id/eprint/5915

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