IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i22p7653-d1283046.html
   My bibliography  Save this article

Research on Vertical SEC Centrifugal Pump Multi-Fault Diagnosis Based on WPT–SVM

Author

Listed:
  • Rongsheng Zhu

    (National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China)

  • Yunpeng Li

    (National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China)

  • Qian Huang

    (China Nuclear Power Engineering Corporation Limited, Beijing 100840, China)

  • Sihan Li

    (National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China)

  • Xinyu Zhang

    (National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China)

  • Huairui Li

    (National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China)

  • Qiang Fu

    (National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China)

Abstract

To diagnose common failures in vertical Essential Service Water Pumps (SEC), a method combining the wavelet packet transform (WPT) and the support vector machine (SVM) was adopted. This allowed us to construct a diagnostic model capable of classifying multiple states, including the six types of faults and normal conditions in SEC pumps. The diagnostic model utilized the wavelet packet coefficients to capture sub-bands with a higher energy share and reconstruct the signals. The model inputs the 12 frequency features into the support vector machine to analyze the vibration signals gathered from the SEC pump benchmark. The study illustrates that the proposed method can accurately differentiate between various fault conditions when compared to the WPT method, combined with the artificial neural network (ANN) approach. It attains a superior overall precision of up to 94%, and it displays excellent generalization and strong adaptability.

Suggested Citation

  • Rongsheng Zhu & Yunpeng Li & Qian Huang & Sihan Li & Xinyu Zhang & Huairui Li & Qiang Fu, 2023. "Research on Vertical SEC Centrifugal Pump Multi-Fault Diagnosis Based on WPT–SVM," Energies, MDPI, vol. 16(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7653-:d:1283046
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/22/7653/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/22/7653/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luís Aguiar-Conraria & Maria Joana Soares, 2014. "The Continuous Wavelet Transform: Moving Beyond Uni- And Bivariate Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 344-375, April.
    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. Roman Mestre, 2021. "A wavelet approach of investing behaviors and their effects on risk exposures," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-37, December.
    2. Yaqi Wu & Chen Zhang & Po Yun & Dandan Zhu & Wei Cao & Zulfiqar Ali Wagan, 2021. "Time–frequency analysis of the interaction mechanism between European carbon and crude oil markets," Energy & Environment, , vol. 32(7), pages 1331-1357, November.
    3. Loretta Mastroeni & Alessandro Mazzoccoli & Greta Quaresima & Pierluigi Vellucci, 2021. "Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect," Papers 2104.11891, arXiv.org, revised Mar 2022.
    4. Panos Fousekis & Vasilis Grigoriadis, 2016. "Price co-movement in the principal skim milk powder producing regions: a wavelet analysis," Economics Bulletin, AccessEcon, vol. 36(1), pages 477-492.
    5. Funashima, Yoshito, 2016. "The Fed-induced political business cycle: Empirical evidence from a time–frequency view," Economic Modelling, Elsevier, vol. 54(C), pages 402-411.
    6. Anand, B. & Paul, Sunil & Ramachandran, M., 2014. "Volatility Spillover between Oil and Stock Market Returns," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 49(1), pages 37-56.
    7. Bolós, V.J. & Benítez, R. & Ferrer, R. & Jammazi, R., 2017. "The windowed scalogram difference: A novel wavelet tool for comparing time series," Applied Mathematics and Computation, Elsevier, vol. 312(C), pages 49-65.
    8. Martyna Marczak & Thomas Beissinger, 2016. "Bidirectional relationship between investor sentiment and excess returns: new evidence from the wavelet perspective," Applied Economics Letters, Taylor & Francis Journals, vol. 23(18), pages 1305-1311, December.
    9. Aloui, Chaker & Jammazi, Rania, 2015. "Dependence and risk assessment for oil prices and exchange rate portfolios: A wavelet based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 62-86.
    10. Rezitis, Anthony N. & Rokopanos, Andreas, 2019. "Impact of trade liberalisation on dairy market price co-movements between the EU, Oceania, and the United States," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 63(3), July.
    11. Paulo Matos & Cristiano Da Silva & Antonio Costa, 2022. "On the relationship between COVID-19 and G7 banking co-movements," Economics Bulletin, AccessEcon, vol. 42(2), pages 793-801.
    12. Habimana, Olivier, 2016. "Oil price, exchange rate and consumer price co-movement: A continuous-wavelet analysis," MPRA Paper 71886, University Library of Munich, Germany.
    13. Mensi, Walid & Rehman, Mobeen Ur & Al-Yahyaee, Khamis Hamed & Al-Jarrah, Idries Mohammad Wanas & Kang, Sang Hoon, 2019. "Time frequency analysis of the commonalities between Bitcoin and major Cryptocurrencies: Portfolio risk management implications," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 283-294.
    14. Manuel Monge & Luis A. Gil-Alana, 2020. "The Lithium Industry and Analysis of the Beta Term Structure of Oil Companies," Risks, MDPI, vol. 8(4), pages 1-17, December.
    15. Mensi, Walid & Ur Rehman, Mobeen & Maitra, Debasish & Hamed Al-Yahyaee, Khamis & Sensoy, Ahmet, 2020. "Does bitcoin co-move and share risk with Sukuk and world and regional Islamic stock markets? Evidence using a time-frequency approach," Research in International Business and Finance, Elsevier, vol. 53(C).
    16. Dewandaru, Ginanjar & Masih, Rumi & Masih, A. Mansur M., 2016. "What can wavelets unveil about the vulnerabilities of monetary integration? A tale of Eurozone stock markets," Economic Modelling, Elsevier, vol. 52(PB), pages 981-996.
    17. Funashima, Yoshito, 2015. "The Fed-Induced Political Business Cycle," MPRA Paper 63654, University Library of Munich, Germany.
    18. Luboš Hanus & Lukáš Vácha, 2020. "Growth cycle synchronization of the Visegrad Four and the European Union," Empirical Economics, Springer, vol. 58(4), pages 1779-1795, April.
    19. repec:zbw:bofrdp:2016_014 is not listed on IDEAS
    20. Caraiani, Petre, 2015. "Estimating DSGE models across time and frequency," Journal of Macroeconomics, Elsevier, vol. 44(C), pages 33-49.
    21. Magazzino, Cosimo & Giolli, Lorenzo, 2021. "The relationship among railway networks, energy consumption, and real added value in Italy. Evidence form ARDL and Wavelet analysis," Research in Transportation Economics, Elsevier, vol. 90(C).

    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:jeners:v:16:y:2023:i:22:p:7653-:d:1283046. 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.