An Arrowhead and Mimosa Based IoT Framework with an Industrial Predictive Maintenance Application

Bulut Barış (Corresponding author), Hasan Burak Ketmen, Ali Serdar Atalay, Oğuzhan Herkiloğlu, Riku Salokangas

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

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.
Original languageEnglish
Title of host publication2021 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA-2021
PublisherIEEE Institute of Electrical and Electronic Engineers
Number of pages5
Publication statusAccepted/In press - 2021
MoE publication typeA4 Article in a conference publication
Event2021 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA-2021 - Kocaeli, Turkey
Duration: 25 Aug 202127 Aug 2021

Conference

Conference2021 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA-2021
Country/TerritoryTurkey
CityKocaeli
Period25/08/2127/08/21

Keywords

  • Arrowhead
  • Mimosa
  • cyber physical systems
  • predictive maintenance
  • industry 4.0

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