| Titre : |
Classification automatique des défauts des moteurs asynchrones |
| Type de document : |
texte imprimé |
| Auteurs : |
Abla Bouguerne, Auteur ; Abdesselam Lebaroud, Directeur de thèse |
| Editeur : |
جامعة الإخوة منتوري قسنطينة |
| Année de publication : |
2017 |
| Importance : |
163 f. |
| Format : |
30 cm. |
| Note générale : |
2 copies imprimées disponibles
|
| Langues : |
Français (fre) |
| Catégories : |
Français - Anglais
Electro-technique
|
| Tags : |
Classification automatique moteurs asynchrones machines électriques |
| Index. décimale : |
622 Electro-Technique |
| Résumé : |
The diagnosis of bearings defects in electric machines has become an important area
of research in recent years. Most studies on the detection of bearing faults using vibration
measurements descended from sensors placed close to the mechanical elements to be
monitored. For this reason, the work of this thesis focused on the automatic classification of
bearing defects of the asynchronous machine with squirrel cage.
We proposed a clustering approach to bearing defects. This approach is based on five
steps: The first step uses the decomposition of the empirical method to separate each vibration
signal in the different intrinsic mode functions, where each mode is in a specific frequency
band. The second step extracts amplitudes and instantaneous frequencies for each mode in
order to identify its frequency band by calculating the Hilbert marginal spectrum. The third
step, the feature extraction phase is carried out by using the energy operator Teager-Kaiser
(TKEO) .In the fourth step, vectors forms extracted was optimized from optimization by
particle swarm which is a stochastic optimization method developed, based on the
reproduction of social behavior. Finally, the final step relates to the automatic classification of
optimized vectors that can be achieved through the Gaussian mixture model algorithm. This
will make the relevance of attributes and automatically selection and classification of bearings
defects in asynchronous electric machines. |
| Diplôme : |
Doctorat en sciences |
| En ligne : |
../theses/electrotec/BOU7032.pdf |
| Format de la ressource électronique : |
pdf |
| Permalink : |
https://bu.umc.edu.dz/md/index.php?lvl=notice_display&id=10567 |
Classification automatique des défauts des moteurs asynchrones [texte imprimé] / Abla Bouguerne, Auteur ; Abdesselam Lebaroud, Directeur de thèse . - جامعة الإخوة منتوري قسنطينة, 2017 . - 163 f. ; 30 cm. 2 copies imprimées disponibles
Langues : Français ( fre)
| Catégories : |
Français - Anglais
Electro-technique
|
| Tags : |
Classification automatique moteurs asynchrones machines électriques |
| Index. décimale : |
622 Electro-Technique |
| Résumé : |
The diagnosis of bearings defects in electric machines has become an important area
of research in recent years. Most studies on the detection of bearing faults using vibration
measurements descended from sensors placed close to the mechanical elements to be
monitored. For this reason, the work of this thesis focused on the automatic classification of
bearing defects of the asynchronous machine with squirrel cage.
We proposed a clustering approach to bearing defects. This approach is based on five
steps: The first step uses the decomposition of the empirical method to separate each vibration
signal in the different intrinsic mode functions, where each mode is in a specific frequency
band. The second step extracts amplitudes and instantaneous frequencies for each mode in
order to identify its frequency band by calculating the Hilbert marginal spectrum. The third
step, the feature extraction phase is carried out by using the energy operator Teager-Kaiser
(TKEO) .In the fourth step, vectors forms extracted was optimized from optimization by
particle swarm which is a stochastic optimization method developed, based on the
reproduction of social behavior. Finally, the final step relates to the automatic classification of
optimized vectors that can be achieved through the Gaussian mixture model algorithm. This
will make the relevance of attributes and automatically selection and classification of bearings
defects in asynchronous electric machines. |
| Diplôme : |
Doctorat en sciences |
| En ligne : |
../theses/electrotec/BOU7032.pdf |
| Format de la ressource électronique : |
pdf |
| Permalink : |
https://bu.umc.edu.dz/md/index.php?lvl=notice_display&id=10567 |
|
Affiner la recherche Interroger des sources externesModèlisation et techniques orientées vers le diagnostic de la machine asynchrone associée à des sources variables / Abdesselam Lebaroud
