IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v15y2024i3d10.1007_s13198-023-02130-9.html
   My bibliography  Save this article

Fault prediction of pneumatic valves in an LNG plant by the DGM(1, 1) model

Author

Listed:
  • Yan Chen

    (Yangtze University)

  • Junyi Qiu

    (Yangtze University)

  • Mengyi Wang

    (Yangtze University)

  • Jiaqi Rao

    (Yumen Oilfield Company, Petrochina)

  • Tian Xia

    (Onshore Operation Area of Jidong Oilfield of Petrochina)

  • Yuan Yang

    (Yangtze University
    Yumen Oilfield Company, Petrochina
    Onshore Operation Area of Jidong Oilfield of Petrochina)

Abstract

Stop production and overhaul is an annual work of LNG (Liquefied Natural Gas) plant. However, the shutdown maintenance work under different market conditions needs comprehensive allocation of resources to achieve the purpose of economy, speed and safety. In order to study the fault situation of pneumatic valve, targeted maintenance work is carried out. In this paper, a workflow of DGM(1, 1) prediction model of grey system and sampling inspection method is proposed. With the help of DGM(1, 1) to predict the universality of "poor information and small sample" and the partial detection method of sampling inspection, the expected engineering purpose is achieved. The research results are as follows: (1) The failure situation of pneumatic valve in LNG plant from 2015 to 2020 is brought into the model to compare the actual value and predicted value in 2020, and the residual error is between 0.92 and 3, which indicates that the predicted result of the model is reliable. It is predicted that the comprehensive failure rate will remain at around 18% in 2021 and 2022. Next, we can continue to use DGM(1, 1) model to analyze the redundancy of actuator measurable signals. Make full use of the field known data, construct the functional relationship of measurable variables of actuators, realize on-line real-time judgment of actuator faults, and dynamically predict the failure rate of parts. (2) For maintenance work, 130 pneumatic valves need to be dismantled, with emphasis on the inspection of three components such as drive gas filter, electromagnetic valve and actuator. If the damage number is found to be less than or equal to 4, production can continue after the spare parts are repaired and replaced, otherwise, total inspection is required. In the next step, the relationship between valve distribution position and parts damage frequency should be counted, and the damage frequency should be reduced by strengthening sealing and changing installation methods.

Suggested Citation

  • Yan Chen & Junyi Qiu & Mengyi Wang & Jiaqi Rao & Tian Xia & Yuan Yang, 2024. "Fault prediction of pneumatic valves in an LNG plant by the DGM(1, 1) model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(3), pages 775-785, March.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:3:d:10.1007_s13198-023-02130-9
    DOI: 10.1007/s13198-023-02130-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-023-02130-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-023-02130-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Angelos Stamos & Sabrina Bruyneel & Bram De Rock & Laurens Cherchye & Siegfried Dewitte, 2018. "A dual-process model of decision-making: The symmetric effect of intuitive and cognitive judgments on optimal budget allocation," ULB Institutional Repository 2013/331388, ULB -- Universite Libre de Bruxelles.
    2. Xu, Ning & Dang, Yaoguo & Gong, Yande, 2017. "Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China," Energy, Elsevier, vol. 118(C), pages 473-480.
    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. Medha Kulkarni & Leena B. Dam & Feeroj Nasirkhan Pathan & Vaibhav V. Vasundekar, 2024. "Evaluating Efficacy of Statutory Disclaimers of Mutual Funds on Novice and Seasoned Investors," Business Perspectives and Research, , vol. 12(1), pages 113-132, January.
    2. Liu, Chong & Wu, Wen-Ze & Xie, Wanli & Zhang, Jun, 2020. "Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    3. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    4. Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
    5. Yuan, Meng & Zhang, Haoran & Wang, Bohong & Huang, Liqiao & Fang, Kai & Liang, Yongtu, 2020. "Downstream oil supply security in China: Policy implications from quantifying the impact of oil import disruption," Energy Policy, Elsevier, vol. 136(C).
    6. Peng Zhang & Xin Ma & Kun She, 2019. "A Novel Power-Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China," Complexity, Hindawi, vol. 2019, pages 1-22, November.
    7. Lin, Jiang & Xu Liu, & Gang He,, 2020. "Regional electricity demand and economic transition in China," Utilities Policy, Elsevier, vol. 64(C).
    8. Yeqi An & Yulin Zhou & Rongrong Li, 2019. "Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods," Energies, MDPI, vol. 12(13), pages 1-24, July.
    9. Sabrina Bruyneel & Laurens Cherchye & Sam Cosaert & Bram De Rock & Siegfried Dewitte, 2020. "Verbal Aptitude Hurts Children’s Economic Decision Making Accuracy," Working Papers ECARES 2020-22, ULB -- Universite Libre de Bruxelles.
    10. Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
    11. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
    12. Li, Shoujun & Miao, Yanzi & Li, Guangyu & Ikram, Muhammad, 2020. "A novel varistructure grey forecasting model with speed adaptation and its application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 172(C), pages 45-70.
    13. Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
    14. Chingfei Luo & Chenzi Liu & Chen Huang & Meilan Qiu & Dewang Li, 2025. "ARIMA Markov Model and Its Application of China’s Total Energy Consumption," Energies, MDPI, vol. 18(11), pages 1-15, June.
    15. Akdi, Yılmaz & Gölveren, Elif & Okkaoğlu, Yasin, 2020. "Daily electrical energy consumption: Periodicity, harmonic regression method and forecasting," Energy, Elsevier, vol. 191(C).
    16. Wu, Lifeng & Gao, Xiaohui & Xiao, Yanli & Yang, Yingjie & Chen, Xiangnan, 2018. "Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China," Energy, Elsevier, vol. 157(C), pages 327-335.
    17. Zhang, Yunxin & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2023. "A novel grey Lotka–Volterra model driven by the mechanism of competition and cooperation for energy consumption forecasting," Energy, Elsevier, vol. 264(C).
    18. Peng Jiang & Yi-Chung Hu & Wenbao Wang & Hang Jiang & Geng Wu, 2020. "Interval Grey Prediction Models with Forecast Combination for Energy Demand Forecasting," Mathematics, MDPI, vol. 8(6), pages 1-12, June.
    19. Ding, Song, 2018. "A novel self-adapting intelligent grey model for forecasting China's natural-gas demand," Energy, Elsevier, vol. 162(C), pages 393-407.
    20. Du, Xiaoyi & Wu, Dongdong & Yan, Yabo, 2023. "Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China," Energy, Elsevier, vol. 262(PA).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:ijsaem:v:15:y:2024:i:3:d:10.1007_s13198-023-02130-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.