Abstract
In order to meet recent challenges for more efficient and economic industrial manufacturing plants and processes, new and already existing infrastructure undergoes transformations towards so called’ Smart Factories’. In this paper a fully integrated Data Analytics Infrastructure is introduced, which is applicable for different use-cases. The modular and scalable infrastructure basically consists of embedded devices for the acquisition of controller signals and process data from the real-time field bus, and a ’private cloud’ server with high storage and computing capacity for data administration, analytics and various other services. The infrastructure’s potential is demonstrated by an exemplary use-case, an energy management approach for multi manipulator handling processes, including monitoring and process optimization functionalities.
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References
1. Weyer, S., Schmitt, M., Ohmer, M., Gorecky, D.: Towards Industry 4.0-Standardization as the crucial challenge for highly modular, multi-vendor production systems. IFAC-PapersOnLine, vol. 48, num. 3, pp. 579–584 (2015).
2. Qin, S.J.: Process data analytics in the era of big data. AIChE Journal, vol. 60, num. 9, pp. 3092–3100 (2014).
3. O’Donovan, P., Leahy, K., Bruton, K., O’Sullivan, D.T.J.: An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. Journal of Big Data, vol. 2, num. 1, pp. 1–22 (2015).
4. Lee, J., Lapira, E., Bagheri, B., Kao, H.: Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, vol. 1, num. 1, pp. 38–41 (2013).
5. Felser, M.: Real-time ethernet industry prospective. In Proceedings of the IEEE, vol. 93, num. 6, pp. 1118–1129 (2005).
6. Steiner, W., Poledna, S.: Fog computing as enabler for the Industrial Internet of Things. e & i Elektrotechnik und Informationstechnik, vol. 133, num. 7, pp. 310–314 (2016).
7. Windmann, S., Maier, A., Niggemann, O., Frey, C., Bernardi, A., Gu, Y., Pfrommer, H., Steckel, T., Krüger, M., Kraus, R.: Big Data Analysis of Manufacturing Processes. Journal of Physics: Conference Series of 12th European Workshop on Advanced Control and Diagnosis, vol. 659, num. 1, pp. 1–12 (2015).
8. Schleipen, M., Kühnert, C., Okon, M., Henßen, R., Bischoff, T.: Mobile Monitoring und smarte Datenanalyse basierend auf offenen Standards. VDI/VDE-Gesellschaft Meß- und Automatisierungstechnik -GMA-, Düsseldorf: Automation (2015).
9. Russom, P.: Big data analytics. TDWI best practices report (2011).
10. Yue, X., Cai, H., Yan, H., Zou, C., Zhou, K.: Cloud-assisted industrial cyberphysical systems: an insight. Microprocessors and Microsystems, vol. 39, num. 8, pp. 1262–1270 (2015).
11. Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., Khan, S.U.: The rise of ”big data” on cloud computing: Review and open research issues. Information Systems, vol. 47, pp. 98–115 (2015).
12. Hallmans, D., Sandström, K., Nolte, T., Larsson, S.: Challenges and opportunities when introducing cloud computing into embedded systems. In Proceedings of the 13th IEEE International Conference on Industrial Informatics (INDIN), pp. 454–459 (2015).
13. Dijcks, J.-P.: Oracle: Big Data for the Enterprise. An Oracle White Paper, Redwood Shores, CA: Oracle Corporation (2013).
14. White, M.: Digital workplaces: vision and reality. Business information review, Sage Publications, vol. 29, num. 4, pp. 205–214 (2012).
15. Vick, A., Horn, C., Rudorfer, M., Krüger, J.: Control of robots and machine tools with an extended factory cloud. In Proceedings of the IEEE World Conference on Factory Communication Systems (WFCS), pp. 1–4 (2015).
16. Kretschmer, F., Borisov, A., Pöschko, R.: Chemnitz, M., Vick, A.: Steuerungstechnik aus der Cloud - Anwendungsszenarien für cloudbasierte Produktion im Rahmen des Forschungsprojekts pICASSO. wt Werkstattstechnik online, vol. 106, num. 5, pp. 308–313 (2016).
17. Jin, X., Wah, B.W., Cheng, X., Wang, Y.: Significance and challenges of big data research. Big Data Research, vol. 2, num. 2, pp. 59–64 (2015).
18. Lee, J., Kao, H., Yang, S.: Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP, vol. 16, pp. 3–8 (2014).
19. The Apache Hadoop Project. http://hadoop.apache.org/core/, Last Access: 03/02/2017
20. White, T.: Hadoop: The Definitive Guide. O’Reilly Media Inc., Sebastapol, CA (2009).
21. Shvachko, K., Hairong, K., Radia, S., Chansler, R.: The Hadoop Distributed File System. In Proceedings of the 26th IEEE Symposium on Mass Storage Systems and Technologies (MSST), pp. 1–10 (2010).
22. Stonebraker, M.: SQL databases v. NoSQL databases. Communications of the ACM, vol. 53, num. 4, pp. 10–11 (2010).
23. Apache Kafka. https://kafka.apache.org/, Last Access: 03/02/2017
24. Apache Storm. http://storm.apache.org/, Last Access: 03/02/2017
25. Apache HBase. https://hbase.apache.org/, Last Access: 03/02/2017
26. Apache Solr. http://lucene.apache.org/solr/, Last Access: 03/02/2017
27. Öltjen, J., Beckmann, D., Hansen, C., Maurer, I., Kotlarski, J., Ortmaier, T.: Integrated Parameter Management Concept for Simplified Implementation of Control, Motion Planning, and Process Optimization Methods. Applied Mechanics & Materials, vol. 840, pp. 114–122, Trans Tech Publications, Schweiz, DOI: 10.4028/www.scientific.net/AMM.840.114 (2016).
28. Hansen, C., Öltjen, J., Meike, D., Ortmaier, T.: Enhanced Approach for Energy-Efficient Trajectory Generation of Industrial Robots. Proceedings of the 8th IEEE International Conference on Automation Science and Engineering, pp. 1–7 (2012).
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Maurer, I., Riva, M., Hansen, C., Ortmaier, T. (2017). Cloud-based Plant and Process Monitoring based on a Modular and Scalable Data Analytics Infrastructure. In: Schüppstuhl, T., Franke, J., Tracht, K. (eds) Tagungsband des 2. Kongresses Montage Handhabung Industrieroboter. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54441-9_4
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DOI: https://doi.org/10.1007/978-3-662-54441-9_4
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