Abstract
Fault diagnosis and condition monitoring of industrial machines have known significant progress in recent years, particularly with the introduction of pattern recognition and data-mining techniques for their development. The decision trees are among the most suitable techniques for the diagnosis and have several algorithms for their construction. Each building algorithm has its advantages and drawbacks which make the optimal choice of adapted method to the desired application difficult. In this paper we propose the diagnosis accomplishment of an industrial ventilator based on the combination vibration analysis-decision trees. For the choice of the adapted decision tree building algorithm a method based on genetic algorithms was used. Its results were commented and discussed
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Karabadji, N.E.I., Khelf, I., Seridi, H., Laouar, L. (2012). Genetic Optimization of Decision Tree Choice for Fault Diagnosis in an Industrial Ventilator. In: Fakhfakh, T., Bartelmus, W., Chaari, F., Zimroz, R., Haddar, M. (eds) Condition Monitoring of Machinery in Non-Stationary Operations. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28768-8_29
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DOI: https://doi.org/10.1007/978-3-642-28768-8_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28767-1
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