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Bearing fault identification of three-phase induction motors bases on two current sensor strategy

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Abstract

Three-phase induction motors are the most commonly used devices for electromechanical energy conversion. This study proposes an alternative approach for identifying bearing faults in induction motors, using two current sensors and a pattern classifier, based on artificial neural networks. To validate the methodology, results are given from experiments carried out on a test bench where the motors operate with different types of bearing faults, under varying conditions of load torque and voltage unbalance. This paper also provides the comparative performance of neural network and random forest classifiers. This study also presents an analysis of the current signals in the time domain, applied to different neural structures.

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Acknowledgments

This study was funded by the contributions of CNPq (Process #552269/2011-5), Araucária Foundation and CAPES (CP 13/2014), CAPES-DS and Federal Technological University of Paraná for their financial support toward the development of this research.

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Authors and Affiliations

  1. Departments of Electrical and Computer Engineering and Mathematics, Federal Technological University of Paraná (UTFPR), Cornélio Procópio, PR, Brazil

    Tiago Drummond Lopes, Alessandro Goedtel, Rodrigo Henrique Cunha Palácios, Wagner Fontes Godoy & Roberto Molina de Souza

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  1. Tiago Drummond Lopes
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  2. Alessandro Goedtel
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  3. Rodrigo Henrique Cunha Palácios
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  4. Wagner Fontes Godoy
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  5. Roberto Molina de Souza
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Correspondence to Tiago Drummond Lopes.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Lopes, T.D., Goedtel, A., Palácios, R.H.C. et al. Bearing fault identification of three-phase induction motors bases on two current sensor strategy. Soft Comput 21, 6673–6685 (2017). https://doi.org/10.1007/s00500-016-2217-8

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  • DOI: https://doi.org/10.1007/s00500-016-2217-8

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