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Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning

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  • He, Deqiang
  • Liu, Chenyu
  • Jin, Zhenzhen
  • Ma, Rui
  • Chen, Yanjun
  • Shan, Sheng

Abstract

Flywheel energy storage system is widely used in train braking energy recovery, and has achieved excellent energy-saving effect. As a key component of the flywheel energy storage system, the health of the bearing is greatly significant to realize the effective recovery of train braking energy. The vibration signal of the bearing presents complex nonlinear and non-stationary characteristics, which makes it difficult to diagnose the fault of the bearing. To solve this problem, a fault diagnosis method for bearing of flywheel energy storage system based on parameter optimization Variational Mode Decomposition (VMD) energy entropy is proposed. Firstly, the improved Sparrow Search Algorithm is used to optimize VMD parameters with the dispersion entropy as the fitness value. Then, the original signal is decomposed into a series of intrinsic mode components by using the optimized VMD algorithm, and the energy entropy of each component is calculated to construct the feature vector. Finally, an Inverted Residual Convolutional Neural Network (IRCNN) is used as feature vector input model for fault diagnosis. The experimental results show that the proposed method can effectively extract the bearing fault characteristics and realize accurate fault diagnosis, and the recognition rate reaches 97.5%, which is better than the comparison method.

Suggested Citation

  • He, Deqiang & Liu, Chenyu & Jin, Zhenzhen & Ma, Rui & Chen, Yanjun & Shan, Sheng, 2022. "Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning," Energy, Elsevier, vol. 239(PB).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221023562
    DOI: 10.1016/j.energy.2021.122108
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    References listed on IDEAS

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    Cited by:

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    2. Zhang, Lang & He, Deqiang & He, Yan & Liu, Bin & Chen, Yanjun & Shan, Sheng, 2022. "Real-time energy saving optimization method for urban rail transit train timetable under delay condition," Energy, Elsevier, vol. 258(C).
    3. Kai Xu & Youguang Guo & Gang Lei & Jianguo Zhu, 2023. "A Review of Flywheel Energy Storage System Technologies," Energies, MDPI, vol. 16(18), pages 1-32, September.
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    6. Li, Jiangkuan & Lin, Meng & Li, Yankai & Wang, Xu, 2022. "Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions," Energy, Elsevier, vol. 254(PB).
    7. Hamid Nasiri & Mohammad Mehdi Ebadzadeh, 2022. "Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition," Papers 2212.14687, arXiv.org.
    8. Zhao, Zhigao & Chen, Fei & He, Xianghui & Lan, Pengfei & Chen, Diyi & Yin, Xiuxing & Yang, Jiandong, 2024. "A universal hydraulic-mechanical diagnostic framework based on feature extraction of abnormal on-field measurements: Application in micro pumped storage system," Applied Energy, Elsevier, vol. 357(C).
    9. Yanzhe Yu & Shijun You & Shen Wei & Huan Zhang & Tianzhen Ye & Yaran Wang & Yanling Na, 2022. "Exploring the Applicability of Building Energy Performance Certification Systems in Underground Stations in China," Sustainability, MDPI, vol. 14(6), pages 1-18, March.

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