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

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

Energy, 2023, vol. 282, issue C

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.

Keywords: Drilling pump; RUL; CNN; CBAM; Transformer (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223018364
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:282:y:2023:i:c:s0360544223018364

DOI: 10.1016/j.energy.2023.128442

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().