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RUL Prediction for Piezoelectric Vibration Sensors Based on Digital-Twin and LSTM Network

Author

Listed:
  • Chengcheng Fu

    (School of Energy and Power Engineering, Beihang University, Beijing 100191, China)

  • Cheng Gao

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Weifang Zhang

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

Abstract

Piezoelectric vibration sensors (PVSs) are widely used in high-temperature environments, such as vibration measurements in aero-engines, because of their high accuracy, small size, and high temperature resistance. Accurate prediction of its RUL (Remaining Useful Life) is essential for applying and maintaining PVSs. Based on PVSs’ characteristics and main failure modes, this work combines the Digital-Twin (DT) and Long Short-Term Memory (LSTM) networks to predict the RUL of PVSs. In this framework, DT can provide rich data collection, analysis, and simulation capabilities, which have advantages in RUL prediction, and LSTM network has good results in predicting time sequence data. The proposed method exploits the advantages of those techniques in feature data collection, sample optimization, and RUL multiclassification. To verify the prediction of this method, a DT platform is established to conduct PVS degradation tests, which generates sample datasets, then the LSTM network is trained and validated. It has been proved that prediction accuracy is more than 99.7%, and training time is within 94 s. Based on this network, the RUL of PVSs is predicted using different test samples. The results show that the method performed well in prediction accuracy, sample data utilization, and compatibility.

Suggested Citation

  • Chengcheng Fu & Cheng Gao & Weifang Zhang, 2024. "RUL Prediction for Piezoelectric Vibration Sensors Based on Digital-Twin and LSTM Network," Mathematics, MDPI, vol. 12(8), pages 1-27, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1229-:d:1378845
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    References listed on IDEAS

    as
    1. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2023. "Parallel State Fusion LSTM-based Early-cycle Stage Lithium-ion Battery RUL Prediction Under Lebesgue Sampling Framework," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    2. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.
    3. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
    4. Salazar, R. & Serrano, M. & Abdelkefi, A., 2020. "Fatigue in piezoelectric ceramic vibrational energy harvesting: A review," Applied Energy, Elsevier, vol. 270(C).
    Full references (including those not matched with items on IDEAS)

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