A Satellite Incipient Fault Detection Method Based on Local Optimum Projection Vector and Kullback-Leibler Divergence

doi:10.3390/app11020797

	A Satellite Incipient Fault Detection Method Based on Local Optimum Projection Vector and Kullback-Leibler Divergence
	Zhang, Ge 1,2; Yang, Qiong 1; Li, Guotong1,2,3 ; Leng, Jiaxing 1; Wang, Long 1
	2021-01
发表期刊	APPLIED SCIENCES-BASEL (IF:2.5[JCR-2023],2.7[5-Year])
EISSN	2076-3417
卷号	11 期号:2 页码:#VALUE!
DOI	10.3390/app11020797
摘要	Timely and effective detection of potential incipient faults in satellites plays an important role in improving their availability and extending their service life. In this paper, the problem of detecting incipient faults using projection vector (PV) and Kullback-Leibler (KL) divergence is studied in the context of detecting incipient faults in satellites. Under the assumption that the variables obey a multidimensional Gaussian distribution and using KL divergence to detect incipient faults, this paper models the optimum PV for detecting incipient faults as an optimization problem. It proves that the PVs obtained by principal component analysis (PCA) are not necessarily the optimum PV for detecting incipient faults. It then compares the on-line probability density function (PDF) with the reference PDF for detecting incipient faults on the local optimum PV. A numerical example and a real satellite fault case were used to assess the validity and superiority of the method proposed in this paper over conventional methods. Since the method takes into account the characteristics of the actual incipient faults, it is more adaptable to various possible incipient faults. Fault detection rates of three simulated faults and the real satellite fault are 98%, 84%, 93% and 92%, respectively.
关键词	Kullback-Leibler (KL) divergence principal component analysis (PCA) optimum projection vector (PV) incipient fault satellite
URL	查看原文
收录类别	SCI ; SCIE
语种	英语
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:000610932900001
出版者	MDPI
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/125933
专题	信息科学与技术学院_特聘教授组_李国通组
通讯作者	Li, Guotong
作者单位	1.Innovat Acad Microsatellites CAS, Shanghai 201203, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 3.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
通讯作者单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Zhang, Ge,Yang, Qiong,Li, Guotong,et al. A Satellite Incipient Fault Detection Method Based on Local Optimum Projection Vector and Kullback-Leibler Divergence[J]. APPLIED SCIENCES-BASEL,2021,11(2):#VALUE!.
APA	Zhang, Ge,Yang, Qiong,Li, Guotong,Leng, Jiaxing,&Wang, Long.(2021).A Satellite Incipient Fault Detection Method Based on Local Optimum Projection Vector and Kullback-Leibler Divergence.APPLIED SCIENCES-BASEL,11(2),#VALUE!.
MLA	Zhang, Ge,et al."A Satellite Incipient Fault Detection Method Based on Local Optimum Projection Vector and Kullback-Leibler Divergence".APPLIED SCIENCES-BASEL 11.2(2021):#VALUE!.