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Estimation of Bearing Fault Severity in Line-Connected and Inverter-Fed Three-Phase Induction Motors

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  • Wagner Fontes Godoy

    (Department of Electrical Engineering, Av. Alberto Carazzai, Federal Technological University of Paraná (UTFPR), 1640, Centro, Cornélio Procópio 86.300-000, PR, Brazil)

  • Daniel Morinigo-Sotelo

    (Department of Electrical Engineering, Paseo del Cauce, 59, University of Valladolid (UVa), 47011 Valladolid, Spain)

  • Oscar Duque-Perez

    (Department of Electrical Engineering, Paseo del Cauce, 59, University of Valladolid (UVa), 47011 Valladolid, Spain)

  • Ivan Nunes da Silva

    (Department of Electrical Engineering, University of São Paulo (USP), São Carlos School of Engineering, Av. Trabalhador São Carlense, 400, Centro, São Carlos 13.566-590, SP, Brazil)

  • Alessandro Goedtel

    (Department of Electrical Engineering, Av. Alberto Carazzai, Federal Technological University of Paraná (UTFPR), 1640, Centro, Cornélio Procópio 86.300-000, PR, Brazil)

  • Rodrigo Henrique Cunha Palácios

    (Department of Electrical Engineering, Av. Alberto Carazzai, Federal Technological University of Paraná (UTFPR), 1640, Centro, Cornélio Procópio 86.300-000, PR, Brazil)

Abstract

This paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management schedule. The detection and estimation of the progressive evolution of a bearing fault are performed by monitoring the amplitude of the current signals at the time domain. Data gathered from line-fed and inverter-fed three-phase induction motors were used to validate the proposed approach. To assess classification accuracy and fault estimation, the models described in this paper are investigated by using Artificial Neural Networks models. The paper also provides process flowcharts and classification tables to present the prognostic models used to estimate the remaining useful life of a defective bearing. Experimental results confirmed the method robustness and provide an accurate diagnosis regardless of the bearing fault stage, motor speed, load level, and type of supply.

Suggested Citation

  • Wagner Fontes Godoy & Daniel Morinigo-Sotelo & Oscar Duque-Perez & Ivan Nunes da Silva & Alessandro Goedtel & Rodrigo Henrique Cunha Palácios, 2020. "Estimation of Bearing Fault Severity in Line-Connected and Inverter-Fed Three-Phase Induction Motors," Energies, MDPI, vol. 13(13), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3481-:d:380748
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    References listed on IDEAS

    as
    1. Hong-Chan Chang & Yu-Ming Jheng & Cheng-Chien Kuo & Yu-Min Hsueh, 2019. "Induction Motors Condition Monitoring System with Fault Diagnosis Using a Hybrid Approach," Energies, MDPI, vol. 12(8), pages 1-12, April.
    2. Zuolu Wang & Jie Yang & Haiyang Li & Dong Zhen & Yuandong Xu & Fengshou Gu, 2019. "Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis," Energies, MDPI, vol. 12(17), pages 1-20, August.
    3. Tengda Huang & Sheng Fu & Haonan Feng & Jiafeng Kuang, 2019. "Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention," Energies, MDPI, vol. 12(20), pages 1-19, October.
    4. Xuejiao Ren & Ruifang Liu & Erle Yang, 2019. "Modelling of the Bearing Breakdown Resistance in Bearing Currents Problem of AC Motors," Energies, MDPI, vol. 12(6), pages 1-9, March.
    5. Marcin Skora & Pawel Ewert & Czeslaw T. Kowalski, 2019. "Selected Rolling Bearing Fault Diagnostic Methods in Wheel Embedded Permanent Magnet Brushless Direct Current Motors," Energies, MDPI, vol. 12(21), pages 1-19, November.
    6. Shrinathan Esakimuthu Pandarakone & Yukio Mizuno & Hisahide Nakamura, 2019. "A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors," Energies, MDPI, vol. 12(11), pages 1-14, June.
    7. Lanjun Wan & Hongyang Li & Yiwei Chen & Changyun Li, 2020. "Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory," Energies, MDPI, vol. 13(5), pages 1-23, March.
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