Abstract
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.
Highlights
Today's business environment sets ever-higher requirements on reliability, availability and economic performance of plants and equipment
The ideal technique is one in which the condition of the equipment is known at all times and which accurately predicts any potential failure on demand
It is important to understand that the event information is only used to determine the critical components and failure modes and to define the normal behavior time periods used as a “baseline” for the health condition assessment. Based on these ideas and considering that the monitored condition is available to a measurement based strategy, we only need to define the Maintenance Threshold (MT), i.e., the asset state or condition when the predictive maintenance action is carried out
Summary
Today's business environment sets ever-higher requirements on reliability, availability and economic performance of plants and equipment. When short-term analysis is needed and when the asset is under different working conditions and regimes, the mean behavior evaluated, based on the events data, is not the best information source to estimate and schedule the “” maintenance actions In this context, measurement based maintenance strategies should be used. It is important to understand that the event information is only used to determine the critical components and failure modes and to define the normal behavior time periods used as a “baseline” for the health condition assessment Based on these ideas and considering that the monitored condition is available to a measurement based strategy, we only need to define the Maintenance Threshold (MT), i.e., the asset state or condition when the predictive maintenance action is carried out. Green CBM takes a triple perspective: 1. Maintenance: Optimizing maintenance strategies based on the prediction of potential failure, scheduling maintenance operations in convenient periods and avoiding unexpected breakdowns
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