Induction Motor Fault Diagnosis Based on the Machine Temperature, Vibration Analysis and Sensors Fusion

Daniel P. Sifuentes Filho; Ygor F. Ginu; Khristian M. de Andrade Jr.; Bernardo P. de Alvarenga; Geyverson T. de Paula

doi:10.18618/REP.e202554

Authors

Daniel P. Sifuentes Filho Federal University of Goiás
Ygor F. Ginu Federal University of Goiás
Khristian M. de Andrade Jr. Federal University of Goiás
Bernardo P. de Alvarenga Federal University of Goiás
Geyverson T. de Paula Federal University of Goiás

DOI:

https://doi.org/10.18618/REP.e202554

Keywords:

Single Phase Induction Motor, Fault Diagnosis, Low Cost, Artificial Neural Network

Abstract

The most common motor used for industrial, residential and commercial applications is the induction motor (three or single phase). This motor is very reliable, but faults still may occur. The present paper focuses on the diagnosis of induction motor faults based on its temperature and vibration behaviors on steady-state operation. The proposed method is based on the Extended Park Transform, enabling sensor fusion which reduces the amount of data required for fault identification to 1/3 and allows the usage of a shallow artificial neural network. To validate the proposed method, experiments have been carried using a single phase induction motor operating under normal and fault conditions (short-circuit between main winding turns, auxiliary turns, main-auxiliary windings and with contaminated bearing lubrication). The results proves the efficacy of the proposed method, which has reached an accuracy over 99.5 % in the process of fault identification using low cost sensors/equipment.

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Author Biographies

Daniel P. Sifuentes Filho, Federal University of Goiás

received the B.Sc degree in electrical engineering from the School of Electrical, Mechanical and Computing Engineering, Federal University of Goiás, Goiânia.

Ygor F. Ginu, Federal University of Goiás

received the B.Sc degree in electrical engineering from the School of Electrical, Mechanical and Computing Engineering, Federal University of Goiás, the B.Sc degree in Mechanical Engineering from Paulista University (UNIP) and the M.Sc degree in Production Engineering from Federal University of Goiás, Goiânia.

Khristian M. de Andrade Jr., Federal University of Goiás

received the B.Sc. and M.Sc. degrees in electrical engineering from the Federal University of Goiás, Goiânia, Brazil, where he is currently pursuing the Ph.D. degree.

Bernardo P. de Alvarenga, Federal University of Goiás

received the B.E. degree from the University of Brasília, Brazil, the M.Sc. degree from the Federal University of Uberlândia, Brazil, and the Ph.D. degree from the University of São Paulo, Brazil, all in electrical engineering. He is currently a Professor with the Federal University of Goiás, Goiânia, Brazil.

Geyverson T. de Paula, Federal University of Goiás

received the B.E., M.Sc., and Ph.D. degrees in electrical engineering from the University of São Paulo, São Carlos, Brazil. He is currently a Professor with the Federal University of Goiás, Goiânia, Brazil.

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