SPAMUF: a behaviour-based maintenance prediction system

Abstract

In the last years we have assisted to several and deep changes in industrial manufacturing. Many industrial processes are now automated in order to ensure the quality of production and to minimize costs. Manufacturing enterprises have been collecting and storing more and more current, detailed and accurate production relevant data. The data stores offer enormous potential as source of new knowledge, but the huge amount of data and its complexity far exceeds the ability to reduce and analyze data without the use of automated analysis techniques. The paper addresses an organizational architecture that integrates data gathered in factories on their activities of reactive, predictive and preventive maintenance. The research is intended to develop a decentralized predictive maintenance system (SPAMUF—Prediction System Failures for Industrial Units Globally Dispersed) based on data mining concepts. Predicting failures more accurately will enable taking appropriate measures to increase reliability.