Fault diagnosis of electrical faults of three-phase induction motors using acoustic analysis

Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are...

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Detalles Bibliográficos
Autores: Glowacz, Adam, Sulowicz, Maciej, Kozik, Jaroslaw, Piech, Krzysztof, Glowacz, Witold, Li, Zhixiong, Brumercik, Frantisek, Gutten, Miroslav, Korenciak, Daniel, Kumar, Anil, Lucas, Guilherme Beraldi [UNESP], Irfan, Muhammad, Caesarendra, Wahyu, Liu, Hui
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:Brasil
Institución:Universidade Estadual Paulista (UNESP)
Repositorio:Repositório Institucional da UNESP
Idioma:inglés
OAI Identifier:oai:repositorio.unesp.br:11449/308305
Acceso en línea:http://dx.doi.org/10.24425/bpasts.2024.148440
https://hdl.handle.net/11449/308305
Access Level:acceso abierto
Palabra clave:acoustic signal
fault
induction motor
neural network.
Descripción
Sumario:Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are low-cost. Changes in the acoustic signal are often observed for faults in induction motors. In this paper, the authors present a fault diagnosis technique for three-phase induction motors (TPIM) using acoustic analysis. The authors analyzed acoustic signals for three conditions of the TPIM: healthy TPIM, TPIM with two broken bars, and TPIM with a faulty ring of the squirrel cage. Acoustic analysis was performed using fast Fourier transform (FFT), a new feature extraction method called MoD-7 (maxima of differences between the conditions), and deep neural networks: GoogLeNet, and ResNet-50. The results of the analysis of acoustic signals were equal to 100% for the three analyzed conditions. The proposed technique is excellent for acoustic signals. The described technique can be used for electric motor fault diagnosis applications.