An Arrowhead and Mimosa Based IoT Framework with an Industrial Predictive Maintenance Application
| Autor: | Ali Serdar Atalay, Hasan Burak Ketmen, Oguzhan Herkiloglu, Baris Bulut, Riku Salokangas |
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| Přispěvatelé: | Kilimci, Zeynep Hilal, Yildirim, Tulay, Piuri, Vincenzo, Czarnowski, Ireneusz, Camacho, David, Manolopoulos, Yannis, Solak, Serdar |
| Rok vydání: | 2021 |
| Předmět: |
System of systems
Mimosa Industry 4.0 business.industry Group method of data handling Computer science Arrowhead Cyber-physical system Intelligent decision support system Predictive maintenance Set (abstract data type) predictive maintenance industry 4.0 Software engineering business cyber physical systems Reusability |
| Zdroj: | INISTA Bulut, B, Burak Ketmen, H, Atalay, A S, Herkiloglu, O & Salokangas, R 2021, An Arrowhead and Mimosa Based IoT Framework with an Industrial Predictive Maintenance Application . in Z H Kilimci, T Yildirim, V Piuri, I Czarnowski, D Camacho, Y Manolopoulos & S Solak (eds), 2021 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA-2021 . IEEE Institute of Electrical and Electronic Engineers, 2021 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021, Kocaeli, Turkey, 25/08/21 . https://doi.org/10.1109/INISTA52262.2021.9548127 |
| DOI: | 10.1109/inista52262.2021.9548127 |
| Popis: | Manufacturing is undergoing an immense change triggered with widespread sensorisation, volumes of data being generated, and advanced machine learning technologies. Problems once solvable via simpler approaches considering more monolithic paradigms have evolved to become larger systems (Cyber Physical Systems; CPS) and Systems of Systems. The scaling, manageability, security, data handling requirements of such systems, as well as the industry’s common goal to reusability have led to several outcomes at the broader European level, Arrowhead and Mimosa being two of those so far. In this study, we consider an Industry 4.0 “Predictive Maintenance” problem. Instead of a rushing with straight data analysis approach as defined under CRISP-DM, we first delve into creating a more widely consumable and reusable set of building blocks by implementing an Arrowhead and Mimosa framework, which together form the route to the machine learning steps that finally lead to the solution. |
| Databáze: | OpenAIRE |
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