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A big data analytical architecture for the Asset Management
Linnaeus University, Faculty of Technology, Department of Informatics.ORCID iD: 0000-0001-7048-8089
IIT Delhi, India.
Mondragon Univ, Spain.
VTT Tech Res Ctr Finland, Finland.
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2017 (English)In: Industrial Product/Service-Systems (IPSS) Conference: Circular Perspectives on Product/Service-Systems / [ed] McAloone, TC Pigosso, DCA Mortensen, NH Shimomura, Y, Elsevier, 2017, p. 369-374Conference paper, Published paper (Refereed)
Abstract [en]

The paper highlights the characteristics of data and big data analytics in manufacturing, more specifically for the industrial asset management. The authors highlight important aspects of the analytical system architecture for purposes of asset management. The authors cover the data and big data technology aspects of the domain of interest. This is followed by application of the big data analytics and technologies, such as machine learning and data mining for asset management. The paper also presents the aspects of visualisation of the results of data analytics. In conclusion, the architecture provides a holistic view of the aspects and requirements of a big data technology application system for purposes of asset management. The issues addressed in the paper, namely equipment health, reliability, effects of unplanned breakdown, etc., are extremely important for today's manufacturing companies. Moreover, the customer's opinion and preferences of the product/services are crucial as it gives an insight into the ways to improve in order to stay competitive in the market. Finally, a successful asset management function plays an important role in the manufacturing industry, which is dependent on the support of proper ICTs for its further success. (C) 2017 The Authors Published by Elsevier B.V.

Place, publisher, year, edition, pages
Elsevier, 2017. p. 369-374
Series
Procedia CIRP, ISSN 2212-8271 ; 64
Keywords [en]
Asset Management, Big data, Big data analytics, Data mining
National Category
Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science, Information Systems
Identifiers
URN: urn:nbn:se:lnu:diva-69674DOI: 10.1016/j.procir.2017.03.019ISI: 000414528200063OAI: oai:DiVA.org:lnu-69674DiVA, id: diva2:1172654
Conference
9th CIRP Industrial Product/Service-Systems (IPSS) Conference - Circular Perspectives on Product/Service-Systems, JUN 19-21, 2017, Copenhagen, DENMARK
Available from: 2018-01-10 Created: 2018-01-10 Last updated: 2018-01-13Bibliographically approved

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Campos, Jaime

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  • apa
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