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Research on Multi-Domain Fault Diagnosis of Gearbox of Wind Turbine Based on Adaptive Variational Mode Decomposition and Extreme Learning Machine Algorithms

Author

Listed:
  • Hui Li

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Bangji Fan

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Rong Jia

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Fang Zhai

    (School of Humanities and Foreign Languages, Xi’an University of Technology, Xi’an 710048, China)

  • Liang Bai

    (Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Xingqi Luo

    (Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710048, China)

Abstract

Since variational mode decomposition (VMD) was proposed, it has been widely used in condition monitoring and fault diagnosis of mechanical equipment. However, the parameters K and α in the VMD algorithm need to be set before decomposition, which causes VMD to be unable to decompose adaptively and obtain the best result for signal decomposition. Therefore, this paper optimizes the VMD algorithm. On this basis, this paper also proposes a method of multi-domain feature extraction of signals and combines an extreme learning machine (ELM) to realize comprehensive and accurate fault diagnosis. First, VMD is optimized according to the improved grey wolf optimizer; second, the feature vectors of the time, frequency, and time-frequency domains are calculated, which are synthesized after dimensionality reduction; ultimately, the synthesized vectors are input into the ELM for training and classification. The experimental results show that the proposed method can decompose the signal adaptively, which produces the best decomposition parameters and results. Moreover, this method can extract the fault features of the signal more completely to realize accurate fault identification.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:6:p:1375-:d:333090
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    References listed on IDEAS

    as
    1. Tang, Baoping & Liu, Wenyi & Song, Tao, 2010. "Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution," Renewable Energy, Elsevier, vol. 35(12), pages 2862-2866.
    2. Gao, Q.W. & Liu, W.Y. & Tang, B.P. & Li, G.J., 2018. "A novel wind turbine fault diagnosis method based on intergral extension load mean decomposition multiscale entropy and least squares support vector machine," Renewable Energy, Elsevier, vol. 116(PA), pages 169-175.
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    Citations

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    Cited by:

    1. Jersson X. Leon-Medina & Francesc Pozo, 2023. "Moving towards Preventive Maintenance in Wind Turbine Structural Control and Health Monitoring," Energies, MDPI, vol. 16(6), pages 1-4, March.
    2. 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.
    3. Len Gelman & Krzysztof Soliński & Andrew Ball, 2021. "Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems," Energies, MDPI, vol. 14(20), pages 1-18, October.
    4. Acarer, Sercan & Uyulan, Çağlar & Karadeniz, Ziya Haktan, 2020. "Optimization of radial inflow wind turbines for urban wind energy harvesting," Energy, Elsevier, vol. 202(C).
    5. Fangqin Zhang & Yan Kang & Xiao Cheng & Peiru Chen & Songbai Song, 2022. "A Hybrid Model Integrating Elman Neural Network with Variational Mode Decomposition and Box–Cox Transformation for Monthly Runoff Time Series Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3673-3697, August.

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