DOI: 10.5937/jaes10-2132
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.
Volume 10 article 227 pages: 107 - 115
Using CM, we have past data for describing damage initiation & development and current data for describing the current condition of a component/machine. Data describing possible behaviour of the
condition in the close future are usually lack in the available commercial
systems although it is important for planning production/maintenance actions,
reducing failures/accidents, & consequently reducing losses & production
cost. In general, accurate maintenance decisions prolong life length of
components/machines and maintain production continuity which adds competitive
advantages. When CM parameter exceeds a significant (warning) level, it demands
a clear understanding of what happened to avoid failures. Also, reliable
answers concerning; what is the probability of failure of a component, its
residual life and when is the most profitable time of conducting maintenance
are necessary. In this paper, a new innovative eMDSS consists of three
strategies for cost-effective maintenance & production processes is
introduced and discussed. The major focus is given on one strategy (Accurate
Maintenance Decisions). This strategy offers opportunity to achieve Just on
Time Dynamic & Cost-effective Maintenance by selecting the most profitable
time for maintenance is developed, tested in case companies and discussed. It
offers unique solutions to increase maintenance profitability by enhancing
maintenance decision accuracy via;
1.
Predicting the value of CM
parameter, e.g. vibration, level, PreVib.
2.
Estimating the probability
of failure at the close future and component residual life,ProLife.
3.
Using this new information,
it will easily estimate the most profitable time for maintenance action.
Accurate Maintenance Decisions provides data about future situation in addition to the current & past data, which is necessary for production and maintenance successful planning. This toolset; predicts the vibration level (PreVib), assesses the probability of failure of equipment and its residual life (ProLife), and consequently it becomes possible to determine the best time of maintenance action. It has been tested in several Swedish and European companies within the branches of car manufacturing, production machine manufacturer and process industry. This study was partly funded by EU-IP DYNAMITE (Dynamic Decisions in Maintenance ) 2005-2009 and partly by E-maintenance Sweden AB. The major conclusions are; applying this toolset it is possible to reduce failures appreciably, prolongs the life length of components/equipment, and perform profitable maintenance.
This study was partly funded by EU-IP DYNAMITE (Dynamic Decisions in Maintenance) 2005-2009 and partly by E-maintenance Sweden AB.
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