A maintenance prediction system using data mining techniques

Abstract

In the last years we have assisted to several and deep changes in industrial companies, mainly due to market dynamics and the need to converge with a globalized and impatient world. These changes are transversal to the entire company also impacting on company maintenance function. In an attempt to eliminate faults and keep systems running without interruption, companies incorporated tools into their Information and Communication Technologies (ICT) systems. The benefits are clear in terms of resulting quality and in costs reduction, particularly those related with the data processing time and accuracy of the resulting knowledge. In their daily routine, companies produce and store endless and complex quantities of data of different nature, increasing the difficulty of use in real time. In this sense, considering the relevance of data collected on industrial plants, namely in its maintenance activities, it is intended with this paper to present a functional architecture of a predictive maintenance system, using data mining techniques on data gathered from manufacturing units globally dispersed. Data Mining will identify behavior patterns, allowing a more accurate early detection of faults in machines. The remote data collection is based on an intricate system of distributed agents, which, given its nature, will be responsible for remote data collection through the functional architecture.