Detection of rotor faults via transient analysis of the external magnetic field - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Detection of rotor faults via transient analysis of the external magnetic field

Résumé

Electric motors condition monitoring is an area that is living a continuous dynamism. There is a strong effort in the search of new, reliable techniques that are able to detect different types of failures and that overcome the drawbacks of the currently available methods. In this regard, vibration and current data analysis are well known techniques that have shown satisfactory results for the detection of certain failures. However, these techniques do not avoid some problems, such as their occasional false indications, which can lead to catastrophic consequences for the involved industries. For instance, this happens when diagnosing rotor faults via current analysis, where there are certain situations that can lead to either false positives or negative indications, such as the presence of oscillating load torques or the existence of non-adjacent bar breakages. Recently, the analysis of the magnetic field in the vicinity of the motor has been proposed as a promising tool to overcome some problems of the classical approaches. However, few works have been focused on the analysis of steady-state signals. This paper explores the analysis of external magnetic fields during transient operation as way to enhance the reliability when detecting rotor faults. The results are promising and show the high potential of this technique to become a complementary information source in cases where the classical tools are not conclusive.
Fichier non déposé

Dates et versions

hal-01899870 , version 1 (19-10-2018)

Identifiants

Citer

José Antonino-Daviu, Hubert Razik, Alfredo Quijano-Lopez, Vicente Climente-Alarcon. Detection of rotor faults via transient analysis of the external magnetic field. 43rd IEEE IECON, Oct 2017, Pékin, China. ⟨10.1109/IECON.2017.8216651⟩. ⟨hal-01899870⟩
68 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More