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Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework

Author

Listed:
  • Hui Li

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710054, China)

  • Fan Li

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710054, China)

  • Rong Jia

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710054, China)

  • Fang Zhai

    (School of Humanities and Foreign Languages, Xi’an University of Technology, Xi’an 710054, China)

  • Liang Bai

    (Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710054, China)

  • Xingqi Luo

    (Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710054, China)

Abstract

Symplectic geometric mode decomposition (SGMD) is a newly proposed signal processing method. Because of its superiority, it has gained more and more attention in the field of fault diagnosis. However, the similar component reorganization problem involved in this method has not been clearly stated. Aiming at this problem, this paper proposes the SGMD-CS method based on the SGMD method and the cosine similarity (CS) and has been compared and verified on the simulation signal and the actual rolling bearing signal. In addition, in order to realize the intelligent diagnosis of the wind turbine bearing fault, the symplectic geometric entropy (SymEn) is extracted as the fault feature and input it into the AdaBoost classification model. In summary, this paper proposes a new wind turbine fault feature extraction method based on the SGMD-CS and AdaBoost framework, and the validity of the method is verified by the rolling bearing vibration data of the Electrical Engineering Laboratory of Case Western Reserve University.

Suggested Citation

  • Hui Li & Fan Li & Rong Jia & Fang Zhai & Liang Bai & Xingqi Luo, 2021. "Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework," Energies, MDPI, vol. 14(6), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1555-:d:515101
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    References listed on IDEAS

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    1. Zhijian Wang & Junyuan Wang & Wenan Cai & Jie Zhou & Wenhua Du & Jingtai Wang & Gaofeng He & Huihui He, 2019. "Application of an Improved Ensemble Local Mean Decomposition Method for Gearbox Composite Fault Diagnosis," Complexity, Hindawi, vol. 2019, pages 1-17, May.
    2. Min Lei & Guang Meng, 2011. "Symplectic Principal Component Analysis: A New Method for Time Series Analysis," Mathematical Problems in Engineering, Hindawi, vol. 2011, pages 1-14, December.
    3. Bin Liu & Peng Zheng & Qilin Dai & Zhongli Zhou, 2018. "The Measurement and Elimination of Mode Splitting: From the Perspective of the Partly Ensemble Empirical Mode Decomposition," Complexity, Hindawi, vol. 2018, pages 1-10, November.
    4. Hui Li & Bangji Fan & Rong Jia & Fang Zhai & Liang Bai & Xingqi Luo, 2020. "Research on Multi-Domain Fault Diagnosis of Gearbox of Wind Turbine Based on Adaptive Variational Mode Decomposition and Extreme Learning Machine Algorithms," Energies, MDPI, vol. 13(6), pages 1-20, March.
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    Cited by:

    1. Wei Ding & Xuguang Zhao & Weigao Meng & Haichao Wang, 2022. "Smart Evaluation of Sustainability of Photovoltaic Projects in the Context of Carbon Neutrality Target," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    2. Zhenen Li & Xinyan Zhang & Tusongjiang Kari & Wei Hu, 2021. "Health Assessment and Remaining Useful Life Prediction of Wind Turbine High-Speed Shaft Bearings," Energies, MDPI, vol. 14(15), pages 1-19, July.

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