Title :

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

Author(s) :

bacchus, Alexandre [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
LAGIS-SI
Biet, Mélisande [Auteur]
Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
Macaire Ludovic, Ludovic Macaire [Auteur]
LAGIS-SI
Le Menach, Yvonnick [Auteur]
Laboratoire d'Électrotechnique et d'Électronique de Puissance (L2EP) - ULR 2697
Tounzi, Abdelmounaim [Auteur]
Laboratoire d'Électrotechnique et d'Électronique de Puissance (L2EP) - ULR 2697

Conference title :

2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)

City :

Valencia

Country :

Espagne

Start date of the conference :

2013-08-27

Book title :

2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)

Publisher :

IEEE

Publication date :

2013-10-24

HAL domain(s) :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]

English abstract : [en]

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 ...
Show more >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%.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Laboratoire d'Électrotechnique et d'Électronique de Puissance (L2EP) - ULR 2697

Research team(s) :

Équipe Outils et Méthodes Numériques

Submission date :

2020-05-15T13:20:55Z
2022-03-08T11:25:13Z
2022-03-08T11:27:03Z