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

Baris Bulut; Hasan Burak Ketmen; Ali Serdar Atalay; Oguzhan Herkiloglu; Riku Salokangas

doi:10.1109/INISTA52262.2021.9548127

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

Baris Bulut
, Hasan Burak Ketmen
, Ali Serdar Atalay
, Oguzhan Herkiloglu
, Riku Salokangas

Enforma Bilişim A.Ş.
Bitnet Bilişim Hizmetleri Ltd

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

3 Citations (Scopus)

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 language	English
Title of host publication	2021 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA-2021
Editors	Zeynep Hilal Kilimci, Tulay Yildirim, Vincenzo Piuri, Ireneusz Czarnowski, David Camacho, Yannis Manolopoulos, Serdar Solak
Publisher	IEEE Institute of Electrical and Electronic Engineers
Number of pages	5
ISBN (Electronic)	978-1-6654-3603-8
ISBN (Print)	978-1-6654-1162-2
DOIs	https://doi.org/10.1109/INISTA52262.2021.9548127
Publication status	Published - 25 Aug 2021
MoE publication type	A4 Article in a conference publication
Event	2021 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 - Kocaeli, Turkey Duration: 25 Aug 2021 → 27 Aug 2021

Conference

Conference	2021 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021
Country/Territory	Turkey
City	Kocaeli
Period	25/08/21 → 27/08/21

Funding

Part of the underlying work that led to this paper has been funded by The Scientific and Technological Research Council of Turkey (TÜBİTAK) and Business Finland under the project entitled “A Smart Predictive Maintenance Approach based on Cyber Physical Systems (SMART-PDM)”, Eureka-ITEA project no: 17041.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Arrowhead
Mimosa
cyber physical systems
predictive maintenance
industry 4.0

Access to Document

10.1109/INISTA52262.2021.9548127

Cite this

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. https://doi.org/10.1109/INISTA52262.2021.9548127

Bulut, Baris ; Burak Ketmen, Hasan ; Atalay, Ali Serdar et al. / An Arrowhead and Mimosa Based IoT Framework with an Industrial Predictive Maintenance Application. 2021 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA-2021. editor / Zeynep Hilal Kilimci ; Tulay Yildirim ; Vincenzo Piuri ; Ireneusz Czarnowski ; David Camacho ; Yannis Manolopoulos ; Serdar Solak. IEEE Institute of Electrical and Electronic Engineers, 2021.

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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{\textquoteright}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.",

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Bulut, B, Burak Ketmen, H, Atalay, AS, Herkiloglu, O & Salokangas, R 2021, An Arrowhead and Mimosa Based IoT Framework with an Industrial Predictive Maintenance Application. in ZH 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

An Arrowhead and Mimosa Based IoT Framework with an Industrial Predictive Maintenance Application. / Bulut, Baris; Burak Ketmen, Hasan; Atalay, Ali Serdar et al.
2021 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA-2021. ed. / Zeynep Hilal Kilimci; Tulay Yildirim; Vincenzo Piuri; Ireneusz Czarnowski; David Camacho; Yannis Manolopoulos; Serdar Solak. IEEE Institute of Electrical and Electronic Engineers, 2021.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - An Arrowhead and Mimosa Based IoT Framework with an Industrial Predictive Maintenance Application

AU - Bulut, Baris

AU - Burak Ketmen, Hasan

AU - Atalay, Ali Serdar

AU - Herkiloglu, Oguzhan

AU - Salokangas, Riku

N1 - Funding Information: Part of the underlying work that led to this paper has been funded by The Scientific and Technological Research Council of Turkey (TÜBİTAK) and Business Finland under the project entitled “A Smart Predictive Maintenance Approach based on Cyber Physical Systems (SMART-PDM)”, Eureka-ITEA project no: 17041.

PY - 2021/8/25

Y1 - 2021/8/25

N2 - 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.

AB - 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.

KW - Arrowhead

KW - Mimosa

KW - cyber physical systems

KW - predictive maintenance

KW - industry 4.0

UR - https://www.scopus.com/pages/publications/85116639716

U2 - 10.1109/INISTA52262.2021.9548127

DO - 10.1109/INISTA52262.2021.9548127

M3 - Conference article in proceedings

AN - SCOPUS:85116639716

SN - 978-1-6654-1162-2

BT - 2021 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA-2021

A2 - Kilimci, Zeynep Hilal

A2 - Yildirim, Tulay

A2 - Piuri, Vincenzo

A2 - Czarnowski, Ireneusz

A2 - Camacho, David

A2 - Manolopoulos, Yannis

A2 - Solak, Serdar

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 2021 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021

Y2 - 25 August 2021 through 27 August 2021

ER -

Bulut B, Burak Ketmen H, Atalay AS, Herkiloglu O, Salokangas R. An Arrowhead and Mimosa Based IoT Framework with an Industrial Predictive Maintenance Application. In Kilimci ZH, Yildirim T, Piuri V, Czarnowski I, Camacho D, Manolopoulos Y, Solak S, editors, 2021 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA-2021. IEEE Institute of Electrical and Electronic Engineers. 2021 doi: 10.1109/INISTA52262.2021.9548127

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

Abstract

Conference

Funding

UN SDGs

Keywords

Access to Document

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