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Satellite on-orbit fault propagation and sweep effect modeling and prediction method and system

A technology of fault propagation and prediction method, which is applied in special data processing applications, design optimization/simulation, and constraint-based CAD, etc. It can solve problems such as gyroscope measurement output out-of-tolerance, control system losing the chance of autonomous fault judgment, satellite attitude deviation, etc.

Active Publication Date: 2021-03-26
NAT UNIV OF DEFENSE TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For example, after the slow-change failure of a satellite's gyro, the attitude of the satellite gradually deviates, and the control system loses the opportunity to judge autonomous faults; as the gyro continues to saturate, the gyro measurement output continues to be out of tolerance, and the satellite enters the full-attitude capture model, making it unable to execute some functions

Method used

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  • Satellite on-orbit fault propagation and sweep effect modeling and prediction method and system
  • Satellite on-orbit fault propagation and sweep effect modeling and prediction method and system
  • Satellite on-orbit fault propagation and sweep effect modeling and prediction method and system

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Embodiment 1

[0129] This embodiment discloses a satellite on-orbit fault propagation and ripple effect modeling and prediction method, such as figure 1 shown, including the following steps:

[0130] Step S1. Collect the historical data of the satellite telemetry parameters, and fill in the missing values ​​by using the previous value filling method.

[0131] Step S2, using the Granger causality model to judge whether there is a Granger causality between the satellite telemetry parameters.

[0132] In this step, the determination of Granger causality between telemetry parameters includes the following steps:

[0133] Step S21, performing mean value removal processing on the telemetry data. In this way, it is ensured that the satellite telemetry data meets the requirement of data fluctuation around the y-axis in the Granger causality test.

[0134] Step S22, selecting the ADF to perform a unit root test on the telemetry data. In order to determine whether the satellite telemetry data mee...

Embodiment 2

[0243] Relying on the methods in the above-mentioned embodiments, this embodiment discloses a specific calculation example of a satellite in-orbit fault propagation and ripple effect modeling and prediction method, including the following steps:

[0244] S11: Collect historical data of satellite telemetry parameters; use the previous value filling method to fill in missing values.

[0245] The parameter data includes telemetry parameters such as temperature, current, voltage, etc., because during the operation of the satellite in orbit, various physical quantities of its components need to be measured. These physical quantities are telemetry parameters, and the obtained data are telemetry data. is a time series, specifically expressed as t is the length of the time series, and n is the number of parameters.

[0246] S12: Use the Granger causality model to determine whether there is a Granger causality between the satellite telemetry parameters. The Granger causality between...

Embodiment 3

[0262] This embodiment discloses a satellite on-orbit fault propagation and ripple effect modeling and prediction system based on the improved Granger causality model, including a memory, a processor, and a computer program stored in the memory and operable on the processor When the processor executes the computer program, the steps of the corresponding methods in the above two embodiments are implemented.

[0263] To sum up, the methods and systems for modeling and predicting satellite on-orbit fault propagation and spillover effects disclosed in the above-mentioned embodiments of the present invention have at least the following beneficial effects:

[0264] The present invention judges the correlation strength, correlation direction, and lag time of the Granger causality between parameters based on the cross-correlation function, and eliminates correlations whose correlation strength is lower than the threshold, and solves the problem that traditional causality modeling canno...

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Abstract

The invention relates to the technical field of satellite state monitoring, and discloses a satellite on-orbit fault propagation and sweep effect modeling and prediction method and system, so as to analyze a fault propagation path of a satellite and ensure the prediction precision. The method comprises the steps of collecting satellite telemetry parameter historical data; using a Granger causalitymodel to determine whether there is a Granger causality between the satellite telemetry parameters; judging the correlation strength, the correlation direction and the lag time of the Granger causality between the parameters based on a cross-correlation function, and removing the correlation with the correlation strength lower than a threshold value; constructing an adjacent matrix according to the relationship among the remaining telemetering parameters after elimination, and drawing a causal relationship graph among the parameters; according to the adjacency matrix between the telemetry parameters, establishing a satellite telemetry parameter fault propagation hierarchy diagram by applying an interpretation structure model; and quantitatively constructing a fault propagation model according to the Granger causality model, and judging the sweep effect of the satellite fault according to the fault propagation model.

Description

technical field [0001] The invention relates to the technical field of satellite state monitoring, in particular to a method and system for modeling and predicting satellite in-orbit fault propagation and ripple effects. Background technique [0002] At present, there are mainly two methods for modeling and predicting fault propagation and ripple effects. One is to start directly from the system architecture and directly construct the communication relationship of system components. The system analysis method uses trees and graphs to represent the system structure, which requires familiarity with the system principles, is relatively complicated, requires a lot of prior knowledge, and is difficult to establish. The other is to analyze the fault propagation relationship of the system from the data point of view, such as Bayesian network, which is currently used more often. But these methods are not always successful in the fault propagation path analysis, and it is difficult ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G06F111/04
CPCG06F30/20G06F2111/04
Inventor 金光陈思雅尤杨马心宇孙鹏
Owner NAT UNIV OF DEFENSE TECH
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