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Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms

Author

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  • Andre S. Barcelos

    (CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, Portugal)

  • Antonio J. Marques Cardoso

    (CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, Portugal)

Abstract

Artificial intelligence algorithms and vibration signature monitoring are recurrent approaches to perform early bearing damage identification in induction motors. This approach is unfeasible in most industrial applications because these machines are unable to perform their nominal functions under damaged conditions. In addition, many machines are installed at inaccessible sites or their housing prevents the setting of new sensors. Otherwise, current signature monitoring is available in most industrial machines because the devices that control, supply and protect these systems use the stator current. Another significant advantage is that the stator phases lose symmetry in bearing damaged conditions and, therefore, are multiple independent sources. Thus, this paper introduces a new approach based on fractional wavelet denoising and a deep learning algorithm to perform a bearing damage diagnosis from stator currents. Several convolutional neural networks extract features from multiple sources to perform supervised learning. An information fusion (IF) algorithm then creates a new feature set and performs the classification. Furthermore, this paper introduces a new method to achieve positive unlabeled learning. The flattened layer of several feature maps inputs the fuzzy c-means algorithm to perform a novelty detection instead of clusterization in a dynamic IF context. Experimental and on-site tests are reported with promising results.

Suggested Citation

  • Andre S. Barcelos & Antonio J. Marques Cardoso, 2021. "Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms," Energies, MDPI, vol. 14(9), pages 1-14, April.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2509-:d:544655
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    References listed on IDEAS

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    1. N. Bessous & S. E. Zouzou & W. Bentrah & S. Sbaa & M. Sahraoui, 2018. "Diagnosis of bearing defects in induction motors using discrete wavelet transform," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(2), pages 335-343, April.
    2. Tianjun Zhang & Shuang Song & Shugang Li & Li Ma & Shaobo Pan & Liyun Han, 2019. "Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series," Energies, MDPI, vol. 12(1), pages 1-15, January.
    3. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez, 2017. "State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors," Energies, MDPI, vol. 10(7), pages 1-34, July.
    4. 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.
    5. Peng Qian & Xiange Tian & Jamil Kanfoud & Joash Lap Yan Lee & Tat-Hean Gan, 2019. "A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network," Energies, MDPI, vol. 12(18), pages 1-15, September.
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    Cited by:

    1. Tongguang Yang & Guanchen Li & Tongyu Wang & Shengyou Yuan & Xueyin Yang & Xiaoguang Yu & Qingkai Han, 2023. "A Novel 1D-Convolutional Spatial-Time Fusion Strategy for Data-Driven Fault Diagnosis of Aero-Hydraulic Pipeline Systems," Mathematics, MDPI, vol. 11(14), pages 1-21, July.
    2. Muhammed Ali Gultekin & Ali Bazzi, 2023. "Review of Fault Detection and Diagnosis Techniques for AC Motor Drives," Energies, MDPI, vol. 16(15), pages 1-22, July.
    3. Tomas Garcia-Calva & Daniel Morinigo-Sotelo & Vanessa Fernandez-Cavero & Rene Romero-Troncoso, 2022. "Early Detection of Faults in Induction Motors—A Review," Energies, MDPI, vol. 15(21), pages 1-18, October.
    4. Hisahide Nakamura & Yukio Mizuno, 2024. "Identification System for Short-Circuit Fault Points in Concentrated Stator Windings of Motors," Energies, MDPI, vol. 17(9), pages 1-14, April.

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