Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms
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- 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.
- 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.
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Cited by:
- 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.
- 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.
- 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.
- 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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Keywords
bearing diagnosis; early damage detection; unlabeled learning; deep learning; dynamic information fusion;All these keywords.
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