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A deep feature learning method for remaining useful life prediction of drilling pumps

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  • Guo, Junyu
  • Wan, Jia-Lun
  • Yang, Yan
  • Dai, Le
  • Tang, Aimin
  • Huang, Bangkui
  • Zhang, Fangfang
  • Li, He

Abstract

Remaining Useful Life (RUL) prediction of drilling pumps, pivotal components in fossil energy production, is essential for efficient maintenance and safe operation of such facilities. This paper introduces a deep feature learning method that combines a Convolutional Neural Network (CNN)-Convolutional Block Attention Module (CBAM) and a Transformer network into a parallel channel method to predict the RUL of drilling pumps. Specifically, two parallel channels independently extract time-frequency domain and time-domain features from strain signals and then proceed with degradation estimation through feature learning. The deep features derived independently from the two channels are subsequently amalgamated to predict the RUL of the drilling pump. The proposed method is validated by the operational data from four operating drilling pumps. The comparative analysis confirms the higher accuracy of the proposed method over several existing state-of-the-art approaches. Overall, the proposed method supports the safe and cost-saving-oriented operation and maintenance of drilling pumps.

Suggested Citation

  • Guo, Junyu & Wan, Jia-Lun & Yang, Yan & Dai, Le & Tang, Aimin & Huang, Bangkui & Zhang, Fangfang & Li, He, 2023. "A deep feature learning method for remaining useful life prediction of drilling pumps," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223018364
    DOI: 10.1016/j.energy.2023.128442
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    Cited by:

    1. Ahmed Sami Alhanaf & Hasan Huseyin Balik & Murtaza Farsadi, 2023. "Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks," Energies, MDPI, vol. 16(22), pages 1-19, November.
    2. Tao Yan & Jizhong Chen & Dong Hui & Xiangjun Li & Delong Zhang, 2024. "The Remaining Useful Life Forecasting Method of Energy Storage Batteries Using Empirical Mode Decomposition to Correct the Forecasting Error of the Long Short-Term Memory Model," Sustainability, MDPI, vol. 16(5), pages 1-14, February.
    3. Przemyslaw Pietrzak & Piotr Pietrzak & Marcin Wolkiewicz, 2024. "Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors," Energies, MDPI, vol. 17(2), pages 1-22, January.
    4. Swarnali Deb Bristi & Mehtar Jahin Tatha & Md. Firoj Ali & Uzair Aslam Bhatti & Subrata K. Sarker & Mehdi Masud & Yazeed Yasin Ghadi & Abdulmohsen Algarni & Dip K. Saha, 2023. "A Meta-Heuristic Sustainable Intelligent Internet of Things Framework for Bearing Fault Diagnosis of Electric Motor under Variable Load Conditions," Sustainability, MDPI, vol. 15(24), pages 1-25, December.
    5. J. N. Chandra Sekhar & Bullarao Domathoti & Ernesto D. R. Santibanez Gonzalez, 2023. "Prediction of Battery Remaining Useful Life Using Machine Learning Algorithms," Sustainability, MDPI, vol. 15(21), pages 1-28, October.

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    Keywords

    Drilling pump; RUL; CNN; CBAM; Transformer;
    All these keywords.

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