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Anomaly Monitoring Method for Key Components of Satellite

机译:卫星关键部件的异常监测方法

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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 (Re) and the charge transfer resistance (Rct) 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 (RX) and healthy residual value (RL) of LIBs based on the state estimation of MSET, and then, through the residual values (RXandRL) 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:本文介绍了卫星关键部件的故障诊断方法,称为异常监测方法(AMM),其基于基于顺序概率率测试(SPRT)的多变量状态估计技术(MSET)和异常检测来组成。在锂离子电池(LIBS)的分析失效的基础上,我们将Libs的失败除以内部故障,外部故障和热失控,以及选择的电解质电阻(RE)和电荷传递电阻(RCT)作为关键参数国家估计。然后,通过Libs的关键参数的实际轨道遥测数据,我们基于MSET的状态估计获得LIB的实际剩余值(RX)和健康残余值(RL),然后通过剩余值(RXANDRL)LIBS,我们根据SPRT的异常检测检测异常状态。最后,我们对LIBS进行了一个例子,并且根据AMM的结果,我们通过将其与阈值检测方法(TDM)的结果进行比较来验证AMM的可行性和有效性。

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