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Intelligent edge processing

: Stojanovic, Ljiljana


Beyerer, Jürgen (Ed.); Maier, A.; Niggemann, O.:
Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2017, 25.10.- 26.10.2017, Lemgo
Berlin: Springer Vieweg, 2020 (Technologien für die intelligente Automation 11)
ISBN: 978-3-662-59083-6 (Print)
ISBN: 978-3-662-59084-3 (Online)
ISBN: 3-662-59083-2
Conference on Machine Learning for Cyber-Physical-Systems and Industry 4.0 (ML4CPS) <3, 2017, Lemgo>
Conference Paper
Fraunhofer IOSB ()
MLIOSB; edge computing; Semantic; intelligent data fusion

Innovating maintenance is crucial for the competitiveness of the European manufacturing, pressured to increase flexibility and efficiency while reducing costs. Initiatives related to Industrie 4.0 have been showing the potential of using advanced/pervasive sensing, big data analytics and cloud-based services. In this paper, we present the edge part of our solution for self-healing manufacturing to early-predict equipment condition and make optimized recommendations for adjustments and maintenance to ensure normal operations. The intelligent edge is advanced, affordable and easily integrated, cyber-physical solution for predicting maintenance of machine tools in varying manufacturing environments, by using new connectivity, sensors and big data analytics methods. The proposed solution is capable to integrate information from many different sources, by including structured, semi-structured and unstructured data. The key innovation is in IoTization through dynamic, multi-modal, smart data gathering and integration based on the semantic technologies.