Abstract
Real time analysis of data collected from the shop floor opens the path towards efficient scheduling of batch execution for large scale distributed manufacturing systems. Prediction of the shop floor activities has a great potential to reduce manufacturing costs, by providing the information required for operational decisions like preventive maintenance, automatic remediation or scheduling optimization. Research has been focusing on how machine learning algorithms can be used to better understand and extract insights from historical data collected from manufacturing systems. However, in the current manufacturing environments, driven by mass customization and short time to market, these approaches fail to be agile enough to be useful. In this paper we propose a real-time machine learning approach for large scale manufacturing systems that can predict various scenarios before service degradation occurs, thus allowing for corrective actions. At the same time, outliner detection algorithms can be used to evaluate the system’s health at a holistic level. Scalability requirements are achieved by modelling the architecture around data streams processed in real time by map-reduce operations. The concepts presented in this paper build on recent developments on flexible, distributed and cloud based manufacturing, where these real time actions can be efficiently implemented.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhong, R.Y., et al.: RFID-enabled real-time manufacturing execution system for mass-customization production. Robot. Comput. Integr. Manuf. 29.2, 283–292 (2013)
Zhang, Y., et al.: Game theory based real-time shop floor scheduling strategy and method for cloud manufacturing. Int. J. Intell. Syst. (2017)
He, Q.P., Wang, J.: Fault detection using the k-nearest neighbour rule for semiconductor manufacturing processes. IEEE Trans. Semicond. Manuf. 20.4, 345–354 (2007)
Viswanadham, N., Johnson, T.L.: Fault detection and diagnosis of automated manufacturing systems. In: Proceedings of the 27th IEEE Conference on, Decision and Control. IEEE (1998)
Heshan, F., Surgenor, B.: An unsupervised artificial neural network versus a rule-based approach for fault detection and identification in an automated assembly machine. Robot. Comput. Integr. Manuf. 43, 79–88 (2017)
Morariu, C., Morariu, O., Borangiu, T.: Manufacturing service bus integration model for implementing highly flexible and scalable manufacturing systems. IFAC Proceedings Volumes 45.6, 1850–1855 (2012)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51.1, 107–113 (2008)
Acknowledgements
This research work has been partially supported by the IBM FA 2016 project: Big Data, Analytics and Cloud for Digital Transformation on Manufacturing—DTM, period of execution 2016–2018.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Morariu, O., Morariu, C., Borangiu, T., Răileanu, S. (2018). Manufacturing Systems at Scale with Big Data Streaming and Online Machine Learning. In: Borangiu, T., Trentesaux, D., Thomas, A., Cardin, O. (eds) Service Orientation in Holonic and Multi-Agent Manufacturing. Studies in Computational Intelligence, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-319-73751-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-73751-5_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73750-8
Online ISBN: 978-3-319-73751-5
eBook Packages: EngineeringEngineering (R0)