Decision tree selection in an industrial machine fault diagnostics

Authors:
Nour El Islem Karabadji;Hassina Seridi;Ilyes Khelf;Lakhdar Laouar
Affiliations:
Electronic Document Management Laboratory (LabGED), Badji Mokhtar-Annaba University, Annaba, Algeria;Electronic Document Management Laboratory (LabGED), Badji Mokhtar-Annaba University, Annaba, Algeria;Laboratoire de Mécanique Industrielle, Badji Mokhtar-Annaba University, Algeria;Laboratoire de Mécanique Industrielle, Badji Mokhtar-Annaba University, Algeria
Venue:
MEDI'12 Proceedings of the 2nd international conference on Model and Data Engineering
Year:
2012

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Abstract

Decision trees are widely used technique in data mining and classification fields. This method classifies objects following succession tests on their attributes. Its principal disadvantage is the choice of optimal model among the various existing trees types (Chaid, Cart,Id3..). Each tree has its specificities which make the choice justification difficult. In this work, decision tree choice validation is studied and the use of genetic algorithms is proposed. To pull out best tree, all models are generated and their performances measured on distinct training and validation sets. After that, various statistical tests are made. In this paper we propose the diagnosis accomplishment of an industrial ventilator(Fan) based an analysis-decision trees.