Few-shot RUL estimation based on model-agnostic meta-learning

Yu Mo (), Liang Li (), Biqing Huang () and Xiu Li ()
Additional contact information
Yu Mo: Tsinghua University
Liang Li: Tsinghua University
Biqing Huang: Tsinghua University
Xiu Li: Tsinghua Shenzhen International Graduate School

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 5, No 17, 2359-2372

Abstract: Abstract Data-driven remaining useful life (RUL) estimation has been a research hotspot in the prognostic and health management (PHM) of industrial equipment and systems. It can achieve predictive maintenance of machinery and rarely require prior expertise in prognostics and signal processing. However, the data-driven methods require sufficient training data, which is difficult to acquire. In this paper, we employ the model-agnostic meta-learning (MAML) algorithm to seek suitable model parameter initialization that can rapidly adapt to the given test sample with few-shot training samples. We also propose to build pseudo-meta-RUL task sets for meta-learning by calculating time sequence similarities. To further improve the applicability of the model, we extend the proposed method from few-shot conditions to general conditions. We conduct experiments on the C-MAPSS dataset and the results show that the proposed algorithm can improve the prediction performance and enhance the generalization ability of the model in the context of few-shot conditions and general conditions.

Keywords: Remaining useful life estimation; Similarity matching; Meta-Learning (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-022-01929-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:joinma:v:34:y:2023:i:5:d:10.1007_s10845-022-01929-w

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-022-01929-w

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().