Fault diagnosis method of link control system for gravitational wave detection

Ai Gao*, Shengnan Xu, Zichen Zhao, Haibin Shang, Rui Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

To maintain the stability of the inter-satellite link for gravitational wave detection, an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed. Different from the traditional fault diagnosis optimization algorithms, the fault intelligent learning method proposed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong coupling nonlinearity. By constructing a two-layer learning network, the method enables efficient joint diagnosis of fault areas and fault parameters. The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s, and the fault diagnosis efficiency is improved by 99.8% compared with the traditional algorithm.

Original languageEnglish
Pages (from-to)922-931
Number of pages10
JournalJournal of Systems Engineering and Electronics
Volume35
Issue number4
DOIs
Publication statusPublished - Aug 2024

Keywords

  • deep learning
  • fault diagnosis
  • gravitational wave detection
  • large scale multi-satellite formation
  • laser link monitoring

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Gao, A., Xu, S., Zhao, Z., Shang, H., & Xu, R. (2024). Fault diagnosis method of link control system for gravitational wave detection. Journal of Systems Engineering and Electronics, 35(4), 922-931. https://doi.org/10.23919/JSEE.2024.000048