TY - GEN
T1 - Intelligent Fault Diagnosis Method of Inertial Sensors for Space Gravitational Wave Detection
AU - Bi, Cheng
AU - Yue, Xiaokui
AU - Ding, Yibo
AU - Dang, Zhaohui
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Fault diagnosis
KW - Fault evaluation
KW - Space inertial sensor
KW - Variational encoder
UR - http://www.scopus.com/inward/record.url?scp=85180622994&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-42515-8_68
DO - 10.1007/978-3-031-42515-8_68
M3 - 会议稿件
AN - SCOPUS:85180622994
SN - 9783031425141
T3 - Mechanisms and Machine Science
SP - 969
EP - 980
BT - Computational and Experimental Simulations in Engineering - Proceedings of ICCES 2023—Volume 1
A2 - Li, Shaofan
PB - Springer Science and Business Media B.V.
T2 - 29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023
Y2 - 26 May 2023 through 29 May 2023
ER -