Paper Info
Title
Fault Detection Method Of Luojia1-01 Satellite Attitude Control System Based On Supervised Local Linear Embedding
Abstract
This paper is aimed at the telemetry data of Luojia1-01 satellite attitude control system, and carries out the research on high-dimensional data feature extraction and fault detection based on supervised local linear embedding (SLLE). Because the general linear feature extraction method can not mine the feature information of nonlinear high-dimensional telemetry data, a data feature extraction and fault detection method based on local linear embedding is designed. Combined with the statistics SPE and T-2, the low-dimensional feature information is obtained for data statistics and monitoring faults. Due to the local linear embedded manifold learning method for the traditional batch processing mode is difficult to update and improve the database online, and the supervised local linear embedding method is introduced. The online sample feature extraction and fault detection schemes are designed, and the database is updated by updating the weight matrix through online samples. Finally, the effectiveness of the method is verified by Luojia1-01 satellite telemetry data. The results show that the fault detection method based on SLLE reduces the false alarm rate (FAR) by approximately 3% and the missing alarm rate (MAR) by approximately 10% compared with local linear embedding (LLE). This method effectively improves the detection capability of the anomalous state of Luojia1-01 satellite and has certain engineering application value.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2932392
IEEE ACCESS
Keywords
DocType
Volume
Luojia1-01, telemetry data, fault detection, supervised local linear embedding (SLLE)
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Zhi Qu194.19
Kai Xu200.68
Zhigang Chen320434.10
Xin He401.35
Yanhao Xie500.34
Mengmeng Liu600.34
Feng Li7115.63
Shuangxue Han800.34