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Modeling and prediction method and system for satellite on-orbit fault propagation and ripple effects

A fault propagation and prediction method technology, applied in special data processing applications, design optimization/simulation, constraint-based CAD, etc., can solve problems such as control system losing the chance of autonomous fault judgment, satellite attitude deviation, and inability to perform functions

Active Publication Date: 2022-05-03
NAT UNIV OF DEFENSE TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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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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  • Modeling and prediction method and system for satellite on-orbit fault propagation and ripple effects
  • Modeling and prediction method and system for satellite on-orbit fault propagation and ripple effects
  • Modeling and prediction method and system for satellite on-orbit fault propagation and ripple effects

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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 method and system for modeling and predicting satellite in-orbit fault propagation and spillover effects, so as to analyze the satellite fault propagation path and ensure prediction accuracy. The method includes: collecting historical data of satellite telemetry parameters; using a Granger causality model to judge whether there is Granger causality between satellite telemetry parameters; judging the correlation strength, correlation Direction, lag time, eliminate the correlation relationship whose correlation strength is lower than the threshold; construct the adjacency matrix according to the relationship between the remaining telemetry parameters after elimination, and draw the causal relationship diagram between the parameters; according to the adjacency matrix between the telemetry parameters, apply Interpret the structural model and establish a fault propagation hierarchy diagram of satellite telemetry parameters; construct a fault propagation model quantitatively based on the Granger causality model, and judge the ripple effect of satellite faults 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 Patents(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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