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A Satellite Incipient Fault Detection Method Based on Local Optimum Projection Vector and Kullback-Leibler Divergence
- Source :
- Applied Sciences, Volume 11, Issue 2, Applied Sciences, Vol 11, Iss 797, p 797 (2021)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- 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.
- Subjects :
- Kullback–Leibler divergence
Computer science
satellite
Kullback-Leibler (KL) divergence
Probability density function
Context (language use)
02 engineering and technology
Fault (geology)
lcsh:Technology
Fault detection and isolation
lcsh:Chemistry
Local optimum
optimum projection vector (PV)
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
principal component analysis (PCA)
Divergence (statistics)
Instrumentation
lcsh:QH301-705.5
Fluid Flow and Transfer Processes
geography
geography.geographical_feature_category
lcsh:T
Process Chemistry and Technology
020208 electrical & electronic engineering
General Engineering
020206 networking & telecommunications
Vector projection
incipient fault
lcsh:QC1-999
Computer Science Applications
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
lcsh:Engineering (General). Civil engineering (General)
Algorithm
lcsh:Physics
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....8dc8850ef7de99db8a54fc2d7c5f8f6d
- Full Text :
- https://doi.org/10.3390/app11020797