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Communication Dans Un Congrès Année : 2013

Comparison of supervised classification algorithms combined with feature extraction and selection: Application to a turbo-generator rotor fault detection

Résumé

The goal of this paper consists in applying pattern recognition methods to turbo-generators. Previous works have shown that a monitor, thanks to pattern recognition, is practical on asynchronous machines. This procedure has rarely taken advantage of these methods for turbogenerator. The statistical model has been obtained from harmonics extracted from flux probes and from stator current and voltage. For this purpose, the main way is to build a learning matrix to predict the functional state of a new measurement. Finally, three classifiers have been compared: k Nearest Neighbors, Linear Discriminant Analysis and Support Vector Machines. The best classification result is obtained by Linear Discriminant Analysis combined with Factorial Discriminant Analysis achieving a score of 84.6%.
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Dates et versions

hal-01937680 , version 1 (28-11-2018)

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Alexandre Bacchus, Mélisande Biet, Ludovic Macaire, Yvonnick Le Menach, Abdelmounaïm Tounzi. Comparison of supervised classification algorithms combined with feature extraction and selection: Application to a turbo-generator rotor fault detection. 2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), 2013, Valencia, Spain. pp.558--565, ⟨10.1109/DEMPED.2013.6645770⟩. ⟨hal-01937680⟩
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