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Anomaly Monitoring Method for Key Components of Satellite

机译:卫星关键部件异常监测方法

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摘要

This paper presented a fault diagnosis method for key components of satellite, called Anomaly Monitoring Method (AMM), which is made up of state estimation based on Multivariate State Estimation Techniques (MSET) and anomaly detection based on Sequential Probability Ratio Test (SPRT). On the basis of analysis failure of lithium-ion batteries (LIBs), we divided the failure of LIBs into internal failure, external failure, and thermal runaway and selected electrolyte resistance (R e) and the charge transfer resistance (R ct) as the key parameters of state estimation. Then, through the actual in-orbit telemetry data of the key parameters of LIBs, we obtained the actual residual value (R X) and healthy residual value (R L) of LIBs based on the state estimation of MSET, and then, through the residual values (R X and R L) of LIBs, we detected the anomaly states based on the anomaly detection of SPRT. Lastly, we conducted an example of AMM for LIBs, and, according to the results of AMM, we validated the feasibility and effectiveness of AMM by comparing it with the results of threshold detective method (TDM).
机译:本文提出了一种针对卫星关键部件的故障诊断方法,称为异常监测方法(AMM),该方法由基于多元状态估计技术(MSET)的状态估计和基于顺序概率比检验(SPRT)的异常检测组成。在分析锂离子电池(LIB)的故障的基础上,我们将LIB的故障分为内部故障,外部故障和热失控,并选择电解质电阻(R e)和电荷转移电阻(R ct)作为状态估计的关键参数。然后,通过LIBs关键参数的实际在轨遥测数据,基于MSET的状态估计,获得LIBs的实际残差值(RX)和健康残差值(RL),然后通过残差值LIB的(RX和RL),我们根据SPRT的异常检测来检测异常状态。最后,我们以LIB的AMM为例,根据AMM的结果,通过与阈值检测方法(TDM)的结果进行比较,验证了AMM的可行性和有效性。

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