AGGREGATION OF ELECTRIC CURRENT CONSUMPTION FEATURES TO EXTRACT MAINTENANCE KPIs
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, differ-ent indicators from the micro to macro level can be calculat...
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doaj-e9805a8489d74f96b5f42b7db303f5b72020-11-24T22:39:59ZengP.A. NOVA S.A.Management Systems in Production Engineering2299-04612017-07-0125318319010.1515/mspe-2017-0027AGGREGATION OF ELECTRIC CURRENT CONSUMPTION FEATURES TO EXTRACT MAINTENANCE KPIsVictor SIMON0Carl-Anders JOHANSSON1Diego GALAR2Luleå University of Technology, SWEDENLuleå University of Technology, SWEDENLuleå University of Technology, SWEDENAll 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, differ-ent 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 con-sumption. 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.http://wydawnictwo.panova.pl/attachments/category/50/mspe-2017-0027.pdffingerprintoperational datacondition based maintenance (CBM)condition monitoring (CM)energy optimizationmachine tool |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Victor SIMON Carl-Anders JOHANSSON Diego GALAR |
spellingShingle |
Victor SIMON Carl-Anders JOHANSSON Diego GALAR AGGREGATION OF ELECTRIC CURRENT CONSUMPTION FEATURES TO EXTRACT MAINTENANCE KPIs Management Systems in Production Engineering fingerprint operational data condition based maintenance (CBM) condition monitoring (CM) energy optimization machine tool |
author_facet |
Victor SIMON Carl-Anders JOHANSSON Diego GALAR |
author_sort |
Victor SIMON |
title |
AGGREGATION OF ELECTRIC CURRENT CONSUMPTION FEATURES TO EXTRACT MAINTENANCE KPIs |
title_short |
AGGREGATION OF ELECTRIC CURRENT CONSUMPTION FEATURES TO EXTRACT MAINTENANCE KPIs |
title_full |
AGGREGATION OF ELECTRIC CURRENT CONSUMPTION FEATURES TO EXTRACT MAINTENANCE KPIs |
title_fullStr |
AGGREGATION OF ELECTRIC CURRENT CONSUMPTION FEATURES TO EXTRACT MAINTENANCE KPIs |
title_full_unstemmed |
AGGREGATION OF ELECTRIC CURRENT CONSUMPTION FEATURES TO EXTRACT MAINTENANCE KPIs |
title_sort |
aggregation of electric current consumption features to extract maintenance kpis |
publisher |
P.A. NOVA S.A. |
series |
Management Systems in Production Engineering |
issn |
2299-0461 |
publishDate |
2017-07-01 |
description |
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, differ-ent 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 con-sumption. 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. |
topic |
fingerprint operational data condition based maintenance (CBM) condition monitoring (CM) energy optimization machine tool |
url |
http://wydawnictwo.panova.pl/attachments/category/50/mspe-2017-0027.pdf |
work_keys_str_mv |
AT victorsimon aggregationofelectriccurrentconsumptionfeaturestoextractmaintenancekpis AT carlandersjohansson aggregationofelectriccurrentconsumptionfeaturestoextractmaintenancekpis AT diegogalar aggregationofelectriccurrentconsumptionfeaturestoextractmaintenancekpis |
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