IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i8p1229-d1378845.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/8/1229/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/8/1229/
    Download Restriction: no
    ---><---

    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. 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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xian, Tongrui & Xu, Yifei & Chen, Chen & Luo, Xiaohui & Zhao, Haixia & Zhang, Yongtao & Shi, Weijie, 2024. "Experimental and theory study on a stacked piezoelectric energy harvester for pressure pulsation in water hydraulic system," Renewable Energy, Elsevier, vol. 225(C).
    2. Fang, Zheng & Tan, Xing & Liu, Genshuo & Zhou, Zijie & Pan, Yajia & Ahmed, Ammar & Zhang, Zutao, 2022. "A novel vibration energy harvesting system integrated with an inertial pendulum for zero-energy sensor applications in freight trains," Applied Energy, Elsevier, vol. 318(C).
    3. Xu, Zhicun & Xie, Naiming & Diao, Huakang, 2023. "Lithium-ion battery state of health monitoring based on an adaptive variable fractional order multivariate grey model," Energy, Elsevier, vol. 283(C).
    4. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2023. "An interpretable state of health estimation method for lithium-ion batteries based on multi-category and multi-stage features," Energy, Elsevier, vol. 283(C).
    5. Guangheng Qi & Ning Ma & Kai Wang, 2024. "Predicting the Remaining Useful Life of Supercapacitors under Different Operating Conditions," Energies, MDPI, vol. 17(11), pages 1-16, May.
    6. Jiang, Wei-Wu & Gao, Xiao-Wei & Xu, Bing-Bing & Lv, Jun, 2023. "Static and forced vibration analysis of layered piezoelectric functionally graded structures based on element differential method," Applied Mathematics and Computation, Elsevier, vol. 437(C).
    7. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2024. "An adaptive and interpretable SOH estimation method for lithium-ion batteries based-on relaxation voltage cross-scale features and multi-LSTM-RFR2," Energy, Elsevier, vol. 304(C).
    8. Ma, Xiao & Zhou, Bo & Xue, Shifeng, 2021. "A meshless Hermite weighted least-square method for piezoelectric structures," Applied Mathematics and Computation, Elsevier, vol. 400(C).
    9. Xing, Zhuoqun & Pan, Yiqun & Yang, Yiting & Yuan, Xiaolei & Liang, Yumin & Huang, Zhizhong, 2024. "Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction," Applied Energy, Elsevier, vol. 365(C).
    10. Manuel Serrano & Kevin Larkin & Sergei Tretiak & Abdessattar Abdelkefi, 2023. "Piezoelectric Energy Harvesting Gyroscopes: Comparative Modeling and Effectiveness," Energies, MDPI, vol. 16(4), pages 1-21, February.
    11. Ryan Salazar & Ryan Quintana & Abdessattar Abdelkefi, 2021. "Role of Electromechanical Coupling, Locomotion Type and Damping on the Effectiveness of Fish-Like Robot Energy Harvesters," Energies, MDPI, vol. 14(3), pages 1-32, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1229-:d:1378845. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.