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Título
Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients
Autor
Año del Documento
2021
Editorial
MDPI
Descripción
Producción Científica
Documento Fuente
Energies, March, 2021, vol. 14, n.5, p. 1469
Abstract
The fault diagnosis of electrical machines during startup transients has received increasing attention regarding the possibility of detecting faults early. Induction motors are no exception, and motor current signature analysis has become one of the most popular techniques for determining the condition of various motor components. However, in the case of inverter powered systems, the condition of a motor is difficult to determine from the stator current because fault signatures could overlap with other signatures produced by the inverter, low-slip operation, load oscillations, and other non-stationary conditions. This paper presents a speed signature analysis methodology for a reliable broken rotor bar diagnosis in inverter-fed induction motors. The proposed fault detection is based on tracking the speed fault signature in the time-frequency domain. As a result, different fault severity levels and load oscillations can be identified. The promising results show that this technique can be a good complement to the classic analysis of current signature analysis and reveals a high potential to overcome some of its drawbacks.
Materias (normalizadas)
Motor de inducción
Materias Unesco
3306.03 Motores Eléctricos
Palabras Clave
Fault detection
Fault diagnosis
Frequency analysis
Induction motors
Rotating machines
Signal processing
Spectral analysis
Time-frequency decompositions
ISSN
1996-1073
Revisión por pares
SI
Patrocinador
Mexican Council of Science and Technology (CONACYT) by the scholarship 487058
Version del Editor
Propietario de los Derechos
Authors
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
Collections
Files in this item
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional