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Predictive Maintenance of Machine Tool Linear Axes: A Case from Manufacturing Industry
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0002-8906-630X
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. KTH Royal Institute of Technology, Stockholm, Sweden. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0001-8679-8049
2018 (engelsk)Inngår i: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 17, s. 118-125Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In sustainable manufacturing, the proper maintenance is crucial to minimise the negative environmental impact. In the context of Cloud Manufacturing, Internet of Things and Big Data, amount of available information is not an issue, the problem is to obtain the relevant information and process them in a useful way. In this paper a maintenance decision support system is presented that utilises information from multiple sources and of a different kind. The key elements of the proposed approach are processing and machine learning method evaluation and selection, as well as estimation of long-term key performance indicators (KPIs) such as a ratio of unplanned breakdowns or a cost of maintenance approach. Presented framework is applied to machine tool linear axes. Statistical models of failures and Condition Based Maintenance (CBM) are built based on data from a population of 29 similar machines from the period of over 4 years and with use of proposed processing approach. Those models are used in simulation to estimate the long-term effect on selected KPIs for different strategies. Simple CBM approach allows, in the considered case, a cost reduction of 40% with the number of breakdowns reduced 6 times in respect to an optimal time-based approach.

sted, utgiver, år, opplag, sider
Elsevier, 2018. Vol. 17, s. 118-125
Emneord [en]
predictive maintenance, condition monitoring, machine tool
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik
Identifikatorer
URN: urn:nbn:se:his:diva-16410DOI: 10.1016/j.promfg.2018.10.022ISI: 000471035200015Scopus ID: 2-s2.0-85060444616OAI: oai:DiVA.org:his-16410DiVA, id: diva2:1264223
Konferanse
28th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2018) June 11-14, 2018, Columbus, OH, USA
Tilgjengelig fra: 2018-11-19 Laget: 2018-11-19 Sist oppdatert: 2019-12-20bibliografisk kontrollert

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