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WorldCist'18 - 6th World Conference on Information Systems and Technologies

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Predictive Maintenance in the Metallurgical Industry: data analysis and feature selection

As a consequence of the increasing competitivity in the current economic environment, Proactive Maintenance practices are gradually becoming more common in industrial environments. In order to implement these practices, large amounts of heterogeneous information must be analysed, such that knowledge about the status of the equipment can be acquired. However, for this data to be of use and before it can be processed by machine learning algorithms, it must go through an exploratory phase. During this step, relationships in the data, redundancy of features and possible meanings can be assessed. In this paper, a number of these procedures are employed, resulting in the discovery of meaningful information. Moreover, a subset of features is selected for future analysis, allowing for the reduction of the feature space from 47 to 32 features.

Marta Fernandes
GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto
Portugal

Alda Canito
GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto
Portugal

Verónica Bolón
Laboratory for Research and Development in Artificial Intelligence (LIDIA), Computer Science Dept., University of A Coruña
Spain

Luís Conceição
GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto
Portugal

Isabel Praça
GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto
Portugal

Goreti Marreiros
GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto
Portugal

 

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