A health baseline construction method for key subsystems and stand-alone correlations based on historical telemetry data

A technology of telemetry data and construction method, which is applied in the direction of complex mathematical operations, etc., which can solve the problems of poor robustness of anomaly detection, insensitivity of early anomalies, and high knowledge dependence, and achieve low computing resource requirements, lightweight stability, and expert knowledge reliance little effect

Active Publication Date: 2022-06-07
BEIHANG UNIV
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Problems solved by technology

[0005] In view of the problems existing in the current satellite health state characterization methods, such as high dependence on expert knowledge, insensitivity to early anomalies, poor robustness of anomaly detection, and too complex model volume, this application aims to propose a key subsystem based on historical telemetry data and A method for building a health baseline based on a single computer

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  • A health baseline construction method for key subsystems and stand-alone correlations based on historical telemetry data
  • A health baseline construction method for key subsystems and stand-alone correlations based on historical telemetry data
  • A health baseline construction method for key subsystems and stand-alone correlations based on historical telemetry data

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Effect test

Embodiment 1

[0143] Example 1. Explanation of the construction process of linear correlation health baseline

[0144] Step 1. Time calibration preprocessing

[0145] Select a set of linearly correlated binary remote parameter sequence combinations, record the "battery 1-9 voltage" remote parameter sequence as X, and record the "battery voltage" remote parameter sequence as Y. Select its working condition-sensitive remote parameter sequence combination, and record the “battery charging current” remote parameter sequence as Z 1 , record the "battery discharge current" remote parameter sequence as Z 2 .

[0146] Its original remote parameter local sequence without time calibration is as follows: figure 2 shown.

[0147] Depend on figure 2 It can be seen that the four original remote parameter sequences without time calibration have different sampling frequencies and sampling times.

[0148] Through the differential processing method described in step 101, m(D 1 )=1, m(D 2 )=1, m(D ...

Embodiment 2

[0166] Embodiment 2. Explanation of the construction process of non-linear correlation health baseline

[0167] Step 1: Time calibration processing. Select a set of non-linearly correlated binary remote parameter sequence combinations, denote the "battery charging current" remote parameter sequence as X, and denote the "battery capacity" remote parameter sequence as Y. Select its working condition-sensitive remote parameter sequence combination, and record the “battery charging current” remote parameter sequence as Z 1 , record the "battery discharge current" remote parameter sequence as Z 2 .

[0168] Its original remote parameter local sequence without time calibration is as follows: Figure 8 shown.

[0169] Depend on Figure 8 It can be seen that the three original remote parameter sequences without time calibration have different sampling frequencies and sampling times.

[0170] Through the differential processing method described in step 101, m(D 1 )=5, m(D 2 )=1...

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Abstract

The present application discloses a key sub-system based on historical telemetry data and a method for constructing a single-machine correlation health baseline, which includes: selecting a set of binary remote reference sequence combinations that are linearly correlated or nonlinearly correlated; The combination of the selected binary remote reference sequence is time-calibrated; the second step is to identify and cut the target working condition for the time-calibrated binary remote parameter sequence, and obtain the binary remote parameter sequence under the target working condition period; the third step , use the binary remote reference sequence data of the normal state target working condition to fit the binary linear correlation health baseline, or use the method based on discrete integration to convert the nonlinear relationship to the linear relationship, and fit the binary nonlinear relationship Relevant health baseline; the binary linear relational health baseline and the binary nonlinear relational health baseline together constitute a relational health baseline library. Through the constructed correlation health baseline library, the quantitative and stable characterization of the key satellite subsystems and stand-alone health status is realized.

Description

technical field [0001] The present application relates to a satellite health monitoring technology, and in particular, to a key subsystem based on historical telemetry data and a method for constructing a single-machine correlation health baseline. Background technique [0002] A satellite is a system with complex functions and composition, including a large number of key subsystems and single units, and a large number of operating status data. By analyzing the operating status data of satellites and monitoring whether they are in abnormal operating state, it is helpful to find and deal with the operating faults of satellites in time, and it is of great significance to ensure the operational reliability of satellites. [0003] To carry out abnormal monitoring of satellites, it is first necessary to accurately, effectively and quantitatively characterize the health status of satellites. The traditional health state characterization methods are mainly divided into three types...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/11G06F17/18
CPCG06F17/11G06F17/18
Inventor 吕琛宋登巍陶来发王自力
Owner BEIHANG UNIV
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