Induction Machines Fault Detection Based on Subspace Spectral Estimation - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Industrial Electronics Année : 2016

Induction Machines Fault Detection Based on Subspace Spectral Estimation

Résumé

—The main objective of this paper is to detect faults in induction machines using a condition monitoring architecture based on stator current measurements. Two types of fault are considered: bearing and broken rotor bars faults. The proposed architecture is based on high-resolution spectral analysis techniques also known as subspace techniques. These frequency estimation techniques allow to separate frequency components including frequencies close to the fundamental one. These frequencies correspond to fault sensitive frequencies. Once frequencies are estimated, their corresponding amplitudes are obtained by using the Least Squares Estimator (LSE). Then, a fault severity criterion is derived from the amplitude estimates. The proposed methods were tested using experimental stator current signals issued from two induction motors with the considered faults. The experimental results show that the proposed architecture has the ability to efficiently and cost-effectively detect faults and identify their severity.
Fichier principal
Vignette du fichier
Trachi_2015.pdf (1.43 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01356659 , version 1 (26-08-2016)

Identifiants

Citer

Youness Trachi, El Houssin El Bouchikhi, Vincent V. Choqueuse, Mohamed Benbouzid. Induction Machines Fault Detection Based on Subspace Spectral Estimation. IEEE Transactions on Industrial Electronics, 2016, 63 (9), ⟨10.1109/TIE.2016.2570741⟩. ⟨hal-01356659⟩
173 Consultations
579 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More