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Aggregation of Electric Current Consumption Features to Extract Maintenance KPIs
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-4107-0991
2017 (English)In: Management Systems in Production Engineering, ISSN 2299-0461, Vol. 25, no 3, p. 183-190Article in journal (Refereed) Published
Abstract [en]

All electric powered machines offer the possibility of extracting information and calculating Key Performance Indicators (KPIs) from the electric current signal. Depending on the time window, sampling frequency and type of analysis, different indicators from the micro to macro level can be calculated for such aspects as maintenance, production, energy consumption etc. On the micro-level, the indicators are generally used for condition monitoring and diagnostics and are normally based on a short time window and a high sampling frequency. The macro indicators are normally based on a longer time window with a slower sampling frequency and are used as indicators for overall performance, cost or consumption. The indicators can be calculated directly from the current signal but can also be based on a combination of information from the current signal and operational data like rpm, position etc. One or several of those indicators can be used for prediction and prognostics of a machine's future behavior. This paper uses this technique to calculate indicators for maintenance and energy optimization in electric powered machines and fleets of machines, especially machine tools.

Place, publisher, year, edition, pages
De Gruyter Open, 2017. Vol. 25, no 3, p. 183-190
National Category
Reliability and Maintenance Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-67213DOI: 10.1515/mspe-2017-0027ISI: 000408931800007OAI: oai:DiVA.org:ltu-67213DiVA, id: diva2:1172474
Note

Validerad;2018;Nivå 2;2018-02-08 (rokbeg)

Available from: 2018-01-10 Created: 2018-01-10 Last updated: 2018-02-08Bibliographically approved

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Simon, VictorJohansson, Carl-AndersGalar, Diego
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