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Arrowhead Framework services for condition monitoring and maintenance based on the open source approach
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för informatik (IK).ORCID-id: 0000-0001-7048-8089
National University of Singapore, Singapore .
Aalborg University, Denmark .
Technical Research Centre of Finland Ltd., Finland .
Vise andre og tillknytning
2019 (engelsk)Inngår i: 6th International Conference on Control Decision Information Technologies, 23rd -26th April, Paris. France, IEEE, 2019, s. 697-702Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The emergence of new Information and Communication Technologies, such as the Internet of Things and big data and data analytics provides opportunities as well as challenges for the domain of interest, and this paper discusses their importance in condition monitoring and maintenance. In addition, the Open system architecture for condition-based maintenance (OSA-CBM), and the Predictive Health Monitoring methods are gone through. Thereafter, the paper uses bearing fault data from a simulation model with the aim to produce vibration signals where different parameters of the model can be controlled. In connection to the former mentioned a prototype was developed and tested for purposes of simulated rolling element bearing fault systems signals with appropriate fault diagnostic and analytics. The prototype was developed taking into consideration recommended standards (e.g., the OSA-CBM). In addition, the authors discuss the possibilities to incorporate the developed prototype into the Arrowhead framework, which would bring possibilities to: analyze various equipment geographically dispersed, especially in this case its rolling element bearing; support servitization of Predictive Health Monitoring methods and large-scale interoperability; and, to facilitate the appearance of novel actors in the area and thus competition.

sted, utgiver, år, opplag, sider
IEEE, 2019. s. 697-702
HSV kategori
Forskningsprogram
Data- och informationsvetenskap
Identifikatorer
URN: urn:nbn:se:lnu:diva-82322DOI: 10.1109/CoDIT.2019.8820366ISBN: 978-1-7281-0521-5 (digital)ISBN: 978-1-7281-0520-8 (tryckt)ISBN: 978-1-7281-0522-2 (tryckt)OAI: oai:DiVA.org:lnu-82322DiVA, id: diva2:1307582
Konferanse
International Conference on Control, Decision and Information Technologies, CoDIT’19, April 23-26, 2019, Paris, France.
Tilgjengelig fra: 2019-04-28 Laget: 2019-04-28 Sist oppdatert: 2019-10-01bibliografisk kontrollert

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