基于抗差估计和改进AIME的缓变故障检测方法

doi:10.12305/j.issn.1001-506X.2022.09.24

Abstract

Abstract:

An improved slowly growing fault detection method which is based on robust estimation and modified autonomous integrity monitoring extrapolation (AIME) is developed in this paper for the purpose of solving the problems that conventional AIME method for integrated navigation system has a big detection delay and it can not judge the ending time of the fault. The method mitigates the influence of fault tracking of Kalman filter by designing an adaptive gain matrix based on standard t-distribution and IGG-Ⅲ (Institute of Geodesy & Geophysics Ⅲ) scheme and proposes the concept of r_A/R statistics, which is composed of fault detection state of AIME and residual chi-square test method (RCTM) fault detection statistics. Then, the outlier of r_A/R sequence is detected through using sample quantile principle, so as to determine the ending time of the slowly growing fault when AIME method has detected the existence of the fault. The simulation result shows that the proposed method can reduce the delay time of fault detection significantly as well as accurately determine the ending time of fault when a slowly growing fault occurs on the integrated system.

Key words: integrated navigation, slowly growing fault, autonomous integrity monitoring extrapolation (AIME), robust estimation, t-distribution, sample quantile

CLC Number:

V249.31

Yingying JIANG, Shuguo PAN, Fei YE, Wang GAO, Chun MA, Hao WANG. Approach for detection of slowly growing fault based on robust estimation and improved AIME[J]. Systems Engineering and Electronics, 2022, 44(9): 2894-2902.

Figures/Tables 11

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Approach for detection of slowly growing fault based on robust estimation and improved AIME

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Figures/Tables 11

References 31

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故障检测法	缓变速率/(m/s)
故障检测法	0.1	0.15	0.25
方案1(M1)	85	54	32
方案2(M2)	81	51	31
方案3(M3)	79	49	29