Abstract
In modern manufacturing, high volumes of data are constantly being generated by the manufacturing processes. However, only a small percentage is actually used in a meaningful way. As part of the H2020 PERFoRM project, which follows the Industry 4.0 vision and targets the seamless reconfiguration of robots and machinery, this paper proposes a framework for the implementation of a highly flexible, pluggable and distributed data acquisition and analysis system, which can be used for both supporting run-time decision making and triggering self-adjustment methods, allowing corrections to be made before failures actually occur, therefore reducing the impact of such events in production.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Batanov, D., Nagarur, N., Nitikhunkasem, P.: Expert-mm: a knowledge-based system for maintenance management. Artif. Intell. Eng. 83–291 (1993)
Bellifemine, F., Poggi, A., Rimassa, G.: Developing multi-agent systems with a fipa-compliant agent framework. Softw. Pract. Experience 103–128 (2001)
Bellifemine, F., Poggi, A., Rimassa, G.: Developing Multi-agent Systems with JADE, pp. 89–103. Springer, Berlin, Heidelberg (2001). http://dx.doi.org/10.1007/3-540-44631-1_7
Caskey, K.R.: A manufacturing problem solving environment combining evaluation, search, and generalisation methods. Comput. Ind. 175–187 (2001)
Choudhary, A.K., Harding, J.A., Tiwari, M.K.: Data mining in manufacturing: a review based on the kind of knowledge. J. Intell. Manuf. 501–521 (2009)
Cushing, R., Belloum, A., Bubak, M., de Laat, C.: Towards a data processing plane: an automata-based distributed dynamic data processing model. Future Gener. Comput. Syst. 21–32 (2016)
Drath, R., Horch, A.: Industrie 4.0: hit or hype? [industry forum]. IEEE Ind. Electron. Mag. 56–58 (2014)
Gilchrist, A.: Introducing industry 4.0. In: Industry 4.0, pp. 195–215. Springer (2016)
Goodhope, K., Koshy, J., Kreps, J., Narkhede, N., Park, R., Rao, J., Ye, V.Y.: Building linkedin’s real-time activity data pipeline. IEEE Data Eng. Bull. 33–45 (2012)
Holden, T., Serearuno, M.: A hybrid artificial intelligence approach for improving yield in precious stone manufacturing. J. Intell. Manuf. 21–38 (2005)
Kreps, J., Narkhede, N., Rao, J., et al.: Kafka: a distributed messaging system for log processing. In: Proceedings of the NetDB, pp. 1–7 (2011)
Lee, J., Kao, H.A., Yang, S.: Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP 3–8 (2014)
Rocha, A.D., Barata, D., Di Orio, G., Santos, T., Barata, J.: Prime as a generic agent based framework to support pluggability and reconfigurability using different technologies. In: Doctoral Conference on Computing, Electrical and Industrial Systems, pp. 101–110. Springer International Publishing (2015)
Rocha, A.D., Peres, R., Barata, J.: An agent based monitoring architecture for plug and produce based manufacturing systems. In: 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), pp. 1318–1323. IEEE (2015)
Rocha, A.D., Peres, R.S., Flores, L., Barata, J.: A multiagent based knowledge extraction framework to support plug and produce capabilities in manufacturing monitoring systems. In: 2015 10th International Symposium on Mechatronics and its Applications (ISMA), pp. 1–5. IEEE (2015)
Stock, T., Seliger, G.: Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP 536–541 (2016)
Wang, G., Koshy, J., Subramanian, S., Paramasivam, K., Zadeh, M., Narkhede, N., Rao, J., Kreps, J., Stein, J.: Building a replicated logging system with apache kafka. Proc. VLDB Endowment 1654–1655 (2015)
Wang, S., Wan, J., Zhang, D., Li, D., Zhang, C.: Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput. Netw. 158–168 (2016)
Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 680435.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Peres, R.S., Rocha, A.D., Coelho, A., Barata Oliveira, J. (2017). A Highly Flexible, Distributed Data Analysis Framework for Industry 4.0 Manufacturing Systems. In: Borangiu, T., Trentesaux, D., Thomas, A., Leitão, P., Oliveira, J. (eds) Service Orientation in Holonic and Multi-Agent Manufacturing . SOHOMA 2016. Studies in Computational Intelligence, vol 694. Springer, Cham. https://doi.org/10.1007/978-3-319-51100-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-51100-9_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51099-6
Online ISBN: 978-3-319-51100-9
eBook Packages: EngineeringEngineering (R0)