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Dynamic feature information extraction using the special empirical mode decomposition entropy value and index energy

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  • Lu, Shibao
  • Ye, Weiwei
  • Xue, Yangang
  • Tang, Yao
  • Guo, Min

Abstract

Based on signal feature extraction, a combination of the empirical mode decomposition (EMD) and index energy methods is adopted in this paper to extract the Draft Tube’s dynamic feature information for the water turbine. Based on the eigenmode component functions derived from EMD of the signal, the index energy is calculated in this paper. Additionally, two model parameters based on indicators of energy are established, and are used as eigenvectors for the fault pattern identification. Taking an example of the pressure fluctuation signal in the water turbine’s draft tube, this method is used to extract the dynamic feature information of the tail pipe, and perform the application testing. The results show that the method is of high accuracy and has not only good quality in extracting eigenvectors but also relatively good accuracy in extracting the dynamic features of complex and special water turbines. This extraction method is effective for fault pattern recognition.

Suggested Citation

  • Lu, Shibao & Ye, Weiwei & Xue, Yangang & Tang, Yao & Guo, Min, 2020. "Dynamic feature information extraction using the special empirical mode decomposition entropy value and index energy," Energy, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:energy:v:193:y:2020:i:c:s0360544219323059
    DOI: 10.1016/j.energy.2019.116610
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    Cited by:

    1. Zheng, Xianghao & Li, Hao & Zhang, Suqi & Zhang, Yuning & Li, Jinwei & Zhang, Yuning & Zhao, Weiqiang, 2023. "Hydrodynamic feature extraction and intelligent identification of flow regimes in vaneless space of a pump turbine using improved empirical wavelet transform and Bayesian optimized convolutional neura," Energy, Elsevier, vol. 282(C).
    2. Li, Jimeng & Cheng, Xing & Peng, Junling & Meng, Zong, 2022. "A new adaptive parallel resonance system based on cascaded feedback model of vibrational resonance and stochastic resonance and its application in fault detection of rolling bearings," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    3. Zheng, Xianghao & Zhang, Suqi & Zhang, Yuning & Li, Jinwei & Zhang, Yuning, 2023. "Dynamic characteristic analysis of pressure pulsations of a pump turbine in turbine mode utilizing variational mode decomposition combined with Hilbert transform," Energy, Elsevier, vol. 280(C).
    4. 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).

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