<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></abstract>

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.

Aggregation of Electric Current Consumption Features to Extract Maintenance KPIs

Management Systems in Production Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"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...

Aggregation of Electric Current Consumption Features to Extract Maintenance KPIs

Article Category: Special Issue Title: Maintenance Performance Measurement and Management Challenges: from Sensing to Decision Support

Online veröffentlicht: 01. Aug. 2017

Seitenbereich: 183 - 190

Eingereicht: 01. Okt. 2016

Akzeptiert: 01. Apr. 2017

DOI: https://doi.org/10.1515/mspe-2017-0027

Schlüsselwörter
fingerprint, operational data, condition based maintenance (CBM), condition monitoring (CM), energy optimization, machine tool

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Aggregation of Electric Current Consumption Features to Extract Maintenance KPIs

Article Category: Special Issue Title: Maintenance Performance Measurement and Management Challenges: from Sensing to Decision Support

Online veröffentlicht: 01. Aug. 2017

Seitenbereich: 183 - 190

Eingereicht: 01. Okt. 2016

Akzeptiert: 01. Apr. 2017

DOI: https://doi.org/10.1515/mspe-2017-0027

Schlüsselwörterfingerprint, operational data, condition based maintenance (CBM), condition monitoring (CM), energy optimization, machine tool

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
fingerprint, operational data, condition based maintenance (CBM), condition monitoring (CM), energy optimization, machine tool