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Data Networking for Industrial Data Analysis Based on a Data Backbone System

Published online by Cambridge University Press:  26 May 2022

A. Eiden ,
T. Eickhoff ,
J. C. Göbel ,
C. Apostolov ,
P. Savarino  and
T. Dickopf
A. Eiden*
Affiliation:
Technische Universität Kaiserslautern, Germany
T. Eickhoff
Affiliation:
Technische Universität Kaiserslautern, Germany
J. C. Göbel
Affiliation:
Technische Universität Kaiserslautern, Germany
C. Apostolov
Affiliation:
CONTACT Software GmbH, Germany
P. Savarino
Affiliation:
CONTACT Software GmbH, Germany
T. Dickopf
Affiliation:
CONTACT Software GmbH, Germany
*
[email protected]

Abstract

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Industrial Data Analytics needs access to huge amounts of data, which is scattered across different IT systems. As part of an integrated reference kit for Industrial Data Analytics, there is a need for a data backend system that provides access to data. This system needs to have solutions for the extraction of data, the management of data and an analysis pipeline for those data. This paper presents an approach for this data backend system.

Keywords

data miningbig data analysisproduct lifecycle management (PLM)data aggregationdata integration
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 693 - 702
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

References

Abele, Eberhard; Reinhart, Gunther (2011): Zukunft der Produktion. Herausforderungen, Forschungsfelder, Chancen. München: Hanser.CrossRefGoogle Scholar
Abramovici, M., Göbel, J.C., Dang, H.B. (2016), Semantic data management for the development and continuous reconfiguration of smart products and systems. In: CIRP Annals 65, Nr. 1, S. 185–188CrossRefGoogle Scholar
Appelrath, H.-J.; Kagermann, H.; Krcmar, H. (2014): Future Business Clouds. Ein Beitrag zum Zukunftsprojekt Internetbasierte Dienste für die Wirtschaft.Google Scholar
Arnarsson, Í.Ö., Gustavsson, E., Malmqvist, J., Jirstrand, M. (2018), Analysis of Engineering Change Requests using Markov Chains. In: Marjanović, D., Štorga, M., Škec, S., Bojčetić, N., Pavković, N. (Eds): Design 2018 Proceedings of the 15th International Design Conference, May 2018, Dubrovnik, Croatia. Zagreb, Fac. of Mechanical Engineering and Naval Architecture Univ, (DS / Design Society, 92), pp. 523–532Google Scholar
Arnarsson, Í.Ö., Malmqvist, J., Gustavsson, E., Jirstrand, M. (2016), Towards big-data analysis of deviation and error reports in product development projects. In: Boks, C. (Eds): Proceedings of NordDesign 2016, August 10-12, 2016, Trondheim, Norway. Bristol, United Kingdom: The Design Society, pp. 83–92Google Scholar
Bauer, N., Stankiewicz, L., Jastrow, M., Horn, D., Teubner, J., Kersting, K., Deuse, J., Weihs, C. (2018), Industrial Data Science: Developing a Qualification Concept for Machine Learning in Industrial Production. In: Archives of Data Science 5, P27 onlineGoogle Scholar
Deuse, J.; Erohin, O.; Lieber, D. (2014): Wissensentdeckung in vernetzten, industriellen Datenbeständen. In: H. Lödding (Hg.): Wie intelligente Vernetzung und kognitive Systeme unsere Arbeit verändern. Berlin: Gito (Schriftenreihe der Hochschulgruppe für Arbeits- und Betriebsorganisation e.V), pp. 373–395.Google Scholar
Eckert, R., Mansel, W., Specht, G. (2005), STEP AP233 + standard PDM = systems engineering PDM? In: 2005 IEEE International Technology Management Conference (ICE): IEEE, pp. 1–8.Google Scholar
Eickelmann, M.; Schallow, J.; Sousanabady, R. Jalali; Deuse, J. (2015a): Lebenszyklusübergreifende Qualitätsservices. Cloudbasierte Service-Plattform zur intelligenten Prognose qualitätsbestimmender Daten. In: ZWF 110 (4), pp. 167–171.Google Scholar
Eickelmann, M.; Wiegand, M.; Konrad, B.; Deuse, J. (2015b): Die Bedeutung von Data-Mining im Kontext von Industrie 4.0. In: ZWF 110 (11), pp. 738–743.CrossRefGoogle Scholar
Eickhoff, T., Eiden, A., Göbel, J.C., Eigner, M. (2020), A Metadata Repository for Semantic Product Lifecycle Management. In: Procedia CIRP 91 (2020), pp. 249–254Google Scholar
Eigner, M., August, U., Schmich, M. (2016), Smarte Produkte erfordern ein Umdenken bei Produktstrukturen und Prozessen : Digitalisireung, Integration, Interdisziplinarität und Föderation.Google Scholar
Eigner, M., Dickopf, T., Apostolov, H., Schaefer, P., Faißt, K.-G., Keßler, A. (2014), System Lifecycle Management: Initial Approach for a Sustainable Product Development Process Based on Methods of Model Based Systems Engineering, Bd. 442. In: Fukuda, S., Bernard, A., Gurumoorthy, B., Bouras, A. (Eds): Product Lifecycle Management for a Global Market. Berlin, Heidelberg, Springer Berlin Heidelberg, 2014 (IFIP Advances in Information and Communication Technology), pp. 287–300Google Scholar
Franke, M., Klein, K., Hribernik, K., Lappe, D., Veigt, M., Thoben, K.-D. (2014), Semantic Web Service Wrappers as a Foundation for Interoperability in Closed-loop Product Lifecycle Management. In: Procedia CIRP 22 (2014), pp. 225–230Google Scholar
Gadatsch, A.; Landrock, H. (2017): Big Data für Entscheider: Entwicklung und Umsetzung datengetriebener Geschäftsmodelle: Springer Fachmedien Wiesbaden.Google Scholar
Geisberger, Eva; Broy, Manfred (2012): agendaCPS. Integrierte Forschungsagenda Cyber-Physical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg.Google Scholar
Harding, J. A.; Shahbaz, M.; Srinivas, ; Kusiak, A. (2006): Data Mining in Manufacturing: A Review. In: Journal of Manufacturing Science and Engineering 128 (4), pp. 969–976.Google Scholar
IBM (2016), Analytics Solution Unified Method: Implementations with Agile principles.Google Scholar
Köksal, G.; Batmaz, İ.; Testik, M. Caner (2011): A review of data mining applications for quality improvement in manufacturing industry. In: Expert Systems with Applications 38 (10), pp. 13448–13467Google Scholar
Müller, P. (2013), Integrated Engineering of Products and Services – Layer-based Development Methodology for Product-Service Systems, Fraunhofer Verlag, Berlin.Google Scholar
Niggemann, O.; Biswas, G.; Kinnebrew, J.; Khorasgani, H.; Volgmann, S.; Bunte, A. (2017): Datenanalyse in der intelligenten Fabrik. In: B. Vogel-Heuser, T. Bauernhansl und M. ten Hompel (Hg.): Handbuch Industrie 4.0 Bd.2. Automatisierung. 2. Aufl. Berlin: Springer Vieweg.CrossRefGoogle Scholar
Quirmbach, O.(2015), Umfrage Änderungsmanagement 2015.Google Scholar
Penciuc, D., Durupt, A., Belkadi, F., Eynard, B., Rowson, H. (2014), Towards a PLM Interoperability for a Collaborative Design Support System. In: Procedia CIRP 25, pp. 369–376Google Scholar
Stark, R.; Neumeyer, S.; Kim, M.; Deuse, J.; Schallow, J. (2014): Status Quo und Handlungsempfehlungen für die digitale Produktentstehung. In: ProduktDatenJournal 21 (2), pp. 12–17.Google Scholar
Strauss, P., Schmitz, M., Wostmann, R., Deuse, J. (2018), Enabling of Predictive Maintenance in the Brownfield through Low-Cost Sensors, an IIoT-Architecture and Machine Learning. In: Abe, N. (Ed.): 2018 IEEE International Conference on Big Data, Dec 10-Dec 13, 2018, Seattle, WA, USA, proceedings, Piscataway, NJ, USA IEEE, pp. 1474–1483CrossRefGoogle Scholar
Woll, R., Hayka, H., Stark, R. (2015), Ontologiebasierte Datenintegration für das Modellbasierte Systems Engineering. In: Maurer, M., Schulze, S.-O., Abulawi, J. (Eds): Tag des Systems Engineering: Bremen, November 12-14, 2014. München, Hanser, pp. 33–42Google Scholar
Wöstmann, R., Barthelmey, A., West, N., Deuse, J. (2019), A Retrofit Approach for Predictive Maintenance. In: Schüppstuhl, T., Tracht, K., Roßmann, J. (Eds.): Tagungsband des 4. Kongresses Montage Handhabung Industrieroboter. 1. Aufl. 2019. Berlin, Heidelberg, Springer Berlin Heidelberg, 2019, pp. 94–106Google Scholar
Wöstmann, R., Schlunder, P., Temme, F., Klinkenberg, R., Kimberger, J., Spichtinger, A., Goldhacker, M., Deuse, J. (2020), Conception of a Reference Architecture for Machine Learning in the Process Industry. In: Wu, X., Jermaine, C., Xiong, L. (Eds.), 2020 IEEE International Conference on Big Data: Dec 10 - Dec 13, 2020, Atlanta, Ga., USA, proceedings. Piscataway, NJ, USA, IEEE, 2020, pp. 1726–1735Google Scholar
Zhan, Y., Tan, K. H., Li, Y., Tse, Y. K. (2018), Unlocking the power of big data in new product development. In: Annals of Operations Research 270, 1–2, pp. 577–595Google Scholar