Intelligent Fault Diagnosis Method of Inertial Sensors for Space Gravitational Wave Detection

Cheng Bi, Xiaokui Yue, Yibo Ding, Zhaohui Dang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Space inertial sensor is one of the key loads in space gravitational wave detection mission. Once it fails, the entire mission is likely to be affected or even fail. The existing data-driven intelligent fault diagnosis methods can effectively diagnose some sensor faults, but it is still difficult to solve the problem that measurement data of space inertial sensor is strong coupling and includes much noise. To solve this issue, this paper proposes a convolutional recurrent variational encoder (CRVAE) for fault diagnosis of space inertial sensors. Specifically, a multilevel feature matrix that represents different time scales is firstly constructed based upon sensor raw data. Subsequently, CRVAE trained by health sensor data encodes the feature matrix, then reconstructs the matrix by decoding. Decoded matrix should restore the original feature matrix as much as possible. Intuitively, the decoded matrix of fault data will hardly restore the original state. By analyzing the residual feature matrix generated by CRVAE, the fault diagnosis of space inertial sensors can be realized. In addition, a fault evaluation function is given in order to estimate the fault severity. The result shows the method of this paper can detect fault timely and accurately, and the proposed fault evaluation function can achieve precisely quantitative analysis of fault severity.

Original languageEnglish
Title of host publicationComputational and Experimental Simulations in Engineering - Proceedings of ICCES 2023—Volume 1
EditorsShaofan Li
PublisherSpringer Science and Business Media B.V.
Pages969-980
Number of pages12
ISBN (Print)9783031425141
DOIs
StatePublished - 2024
Event29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023 - Shenzhen, China
Duration: 26 May 202329 May 2023

Publication series

NameMechanisms and Machine Science
Volume143
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992

Conference

Conference29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023
Country/TerritoryChina
CityShenzhen
Period26/05/2329/05/23

Keywords

  • Fault diagnosis
  • Fault evaluation
  • Space inertial sensor
  • Variational encoder

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