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Entropy
Volume 23
Issue 9
10.3390/e23091194
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Article

A Satellite Incipient Fault Detection Method Based on Decomposed Kullback–Leibler Divergence

by
Ge Zhang
1,2,
Qiong Yang
1,
Guotong Li
1,2,3,*,
Jiaxing Leng
1 and
Mubiao Yan
1,2
1
Innovation Academy for Microsatellites of CAS, Shanghai 201203, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
*
Author to whom correspondence should be addressed.
Entropy 2021, 23(9), 1194; https://doi.org/10.3390/e23091194
Submission received: 30 July 2021 / Revised: 6 September 2021 / Accepted: 7 September 2021 / Published: 9 September 2021
(This article belongs to the Special Issue Fault Diagnosis Method Based on Information Theoretic: From Theory to Applications)
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Abstract

Detection of faults at the incipient stage is critical to improving the availability and continuity of satellite services. The application of a local optimum projection vector and the Kullback–Leibler (KL) divergence can improve the detection rate of incipient faults. However, this suffers from the problem of high time complexity. We propose decomposing the KL divergence in the original optimization model and applying the property of the generalized Rayleigh quotient to reduce time complexity. Additionally, we establish two distribution models for subfunctions F1(w) and F3(w) to detect the slight anomalous behavior of the mean and covariance. The effectiveness of the proposed method was verified through a numerical simulation case and a real satellite fault case. The results demonstrate the advantages of low computational complexity and high sensitivity to incipient faults.
Keywords: Kullback–Leibler (KL) divergence; fault detection; condition monitoring; incipient fault; generalized Rayleigh quotient (GRQ); optimum projection vector (PV) Kullback–Leibler (KL) divergence; fault detection; condition monitoring; incipient fault; generalized Rayleigh quotient (GRQ); optimum projection vector (PV)

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MDPI and ACS Style

Zhang, G.; Yang, Q.; Li, G.; Leng, J.; Yan, M. A Satellite Incipient Fault Detection Method Based on Decomposed Kullback–Leibler Divergence. Entropy 2021, 23, 1194. https://doi.org/10.3390/e23091194

AMA Style

Zhang G, Yang Q, Li G, Leng J, Yan M. A Satellite Incipient Fault Detection Method Based on Decomposed Kullback–Leibler Divergence. Entropy. 2021; 23(9):1194. https://doi.org/10.3390/e23091194

Chicago/Turabian Style

Zhang, Ge, Qiong Yang, Guotong Li, Jiaxing Leng, and Mubiao Yan. 2021. "A Satellite Incipient Fault Detection Method Based on Decomposed Kullback–Leibler Divergence" Entropy 23, no. 9: 1194. https://doi.org/10.3390/e23091194

APA Style

Zhang, G., Yang, Q., Li, G., Leng, J., & Yan, M. (2021). A Satellite Incipient Fault Detection Method Based on Decomposed Kullback–Leibler Divergence. Entropy, 23(9), 1194. https://doi.org/10.3390/e23091194

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