Diagnosis of Broken Rotor Bars during the Startup of Inverter-Fed Induction Motors Using the Dragon Transform and Functional ANOVA

[EN] A proper diagnosis of the state of an induction motor is of great interest to industry given the great importance of the extended use of this motor. Presently, the use of this motor driven by a frequency converter is very widespread. However, operation by means of an inverter introduces certain...

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Detalles Bibliográficos
Autores: Fernández-Cavero, Vanesa, García-Escudero, Luis A., Fernández-Temprano, Miguel A., Duque-Perez, Oscar, Morinigo-Sotelo, Daniel, Pons Llinares, Joan|||0000-0003-3756-1242
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/193518
Acceso en línea:https://riunet.upv.es/handle/10251/193518
Access Level:acceso abierto
Palabra clave:Induction motors
Transient analysis
Fault diagnosis
Functional ANOVA
INGENIERIA ELECTRICA
Descripción
Sumario:[EN] A proper diagnosis of the state of an induction motor is of great interest to industry given the great importance of the extended use of this motor. Presently, the use of this motor driven by a frequency converter is very widespread. However, operation by means of an inverter introduces certain difficulties for a correct diagnosis, which results in a signal with higher harmonic content and noise level, which makes it difficult to perform a correct diagnosis. To solve these problems, this article proposes the use of a time-frequency technique known as Dragon Transform together with the functional ANOVA statistical technique to carry out a proper diagnosis of the state of the motor by working directly with the curves obtained from the application of the transform. A case study is presented showing the good results obtained by applying the methodology in which the state of the rotor bars of an inverter-fed motor is diagnosed considering three failure states and operating at different load levels.