Application of data mining in a maintenance system for failure prediction
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abstract
In industrial environment, data generated during equipment maintenance and monitoring
activities has become increasingly overwhelming. Data mining presents an opportunity to increase
significantly the rate at which the volume of data can be turned into useful information. This paper
presents an architecture designed to gather data generated in industrial units on their maintenance
activities, and to forecast future failures based on data analysis. Rapid Miner is used to apply different
data mining prediction algorithms to maintenance data and compare their accuracy in the discovery of
patterns and predictions. The tool is integrated with an online system which collects data using automatic
agents and presents all the results to the maintenance teams. The purpose of the prediction algorithms is
to forecast future values based on present records, in order to estimate the possibility of a machine breakdown
and therefore to support maintenance teams in planning appropriate maintenance interventions.
