Abstract
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.
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References
1. Big Data in Manufacturing: BDA and IoT Can Optimize Production Lines and the Bottom Line— but Much of the Industry Isn’t There Yet, Frost & Sullivan, Big Data & Analytics, December 2016
2. Big Data, XaaS, and IoT Transforming Manufacturing Automation, Disruptive Technologies Transforming Traditional Processes to Enable Smart Manufacturing, July 2016
3. M. Schleipen, et al.: Requirements and concept for Plug&Work. Automatisierungstechnik 63:801-820, 2015
4. D. Riemer, et al, StreamPipes: solving the challenge with semantic stream pipelines. DEBS 2015: 330-331
L. Stojanovic, et al., Big-data-driven anomaly detection in industry (4.0): An approach and a case study. BigData 2016: 1647-1652
6. L. Stojanovic, et al., PREMIuM: Big Data Platform for enabling Self-healing Manufacturing, ICE 2017
7. F: Ganz, Automated Semantic Knowledge Acquisition from Sensor Data; IEEE Systems Special Issue, 2016
8. R.Volz, , et al.,: Unveiling the hidden bride: deep annotation for mapping and migrating legacy data to the Semantic Web. J. Web Sem. 1(2): 187-206 (2004)
9. N Stojanovic, et al.,: Semantic Complex Event Reasoning - Beyond Complex Event Processing. Foundations for the Web of Information and Services 2011: 253-279
10. I. Grangel-González, et al.: Towards a Semantic Administrative Shell for Industry 4.0 Components, ICSC, Seite 230-237. IEEE Computer Society, (2016)
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Stojanovic, L. (2020). Intelligent edge processing. In: Beyerer, J., Maier, A., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 11. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-59084-3_5
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DOI: https://doi.org/10.1007/978-3-662-59084-3_5
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Publisher Name: Springer Vieweg, Berlin, Heidelberg
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Online ISBN: 978-3-662-59084-3
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