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Satellite telemetry data abnormity detection method based on DTW

A satellite telemetry data and anomaly detection technology, which is applied in the field of satellite telemetry data anomaly detection, can solve the problems of missing abnormal detection of satellite components, inaccurate abnormal detection results, and inaccurate time series measurement results, etc., to achieve the effect of solving abnormal missing detection

Active Publication Date: 2015-09-16
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the problem that the abnormal parameters of the existing detection method do not exceed the alarm threshold, which causes the abnormal missed detection of satellite components, and the problem that there is a large deviation in the segmentation of satellite telemetry data with periodic characteristics with a fixed number of points There is a slight deviation from the time series, which makes the measurement results inaccurate and leads to inaccurate anomaly detection results.

Method used

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  • Satellite telemetry data abnormity detection method based on DTW
  • Satellite telemetry data abnormity detection method based on DTW
  • Satellite telemetry data abnormity detection method based on DTW

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specific Embodiment approach 1

[0037] Specific implementation mode one: combine figure 1 Illustrate this embodiment, the method for abnormal detection of satellite telemetry data based on DTW, comprises the following steps:

[0038] Step 1: Segment with the point of the argument mutation point as the mark, and obtain the time series containing the category label, including the time series X={x 1 ,x 2 ,...,x n}, the corresponding category label L={l 1 , l 2 ,...,l n}; where n is the number of time series containing category labels, l=1, 2, ..., c; c is the number of categories;

[0039] Step 2: Calculate the average DTW distance within each category in the time series containing category labels S ‾ = { s ‾ 1 , s ‾ 2 , ... , s ‾ ...

specific Embodiment approach 2

[0049] Specific implementation mode two: the specific implementation steps of step one described in this implementation mode are as follows:

[0050] Step 1.1: Segment the historical satellite telemetry data under the normal operating state of the satellite with the point of change in argument angle as the mark, and obtain the normal time series X={x 1 ,x 2 ,...,x n}; Argument is one of the test parameters in satellite telemetry data, and its changing law is increasing from 0 to 360. When it reaches 360, it becomes 0 and starts to increase again. The point from 360 to 0 is the argument Sudden point: Segmentation is carried out with the mark of the sudden change point of the argument angle, that is: record the corresponding time of the sudden change point of the argument angle, and extract other test parameters according to the time corresponding to the sudden change point of the argument angle, the extraction method is two adjacent sudden change points of the argument angle ...

specific Embodiment approach 3

[0053] Specific implementation mode three: the specific implementation steps of step five described in this embodiment mode are as follows:

[0054] Step 5.1: Determine the K time series containing category labels that are closest to the DTW of the time series x' to be detected, that is, when D={d 1 , d 2 ,...,d n}, take the K smallest values, and determine the time series containing the category labels corresponding to the K smallest values; the corresponding category labels are

[0055] Step 5.2: Statistical category labels The category with the highest frequency in , that is, the quasi-category is l'.

[0056] Other steps and parameters are the same as in the second embodiment.

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Abstract

The invention provides a satellite telemetry data abnormity detection method based on the DTW, and relates to the field of satellite telemetry data abnormity detection. The problems that by means of an existing detection method, missing satellite part abnormity detection is caused when abnormity parameters do not exceed alarm thresholds, large deviation exists when segmentation is conducted on satellite telemetry data with periodic characteristics according to the fixed point number, and the abnormity detection result is not accurate enough when tiny offset happens to the time sequence and the measurement result is not accurate enough are solved. The method includes the steps of conducting segmentation on satellite telemetry data with argument sudden change points as marks, calculating the average DTW distance value in various categories in the time sequence containing category labels, obtaining a time sequence x' where abnormity detection is to be conducted, calculating the DTW distance between x' and the time sequence X containing the category labels and determining the minimum DTW distance dmin between the x' and the quasi category l', and determining whether x' is an abnormal sequence or not according to the relation between dmin and the formula (please see the formula in the specification). The method is suitable for satellite telemetry data abnormity detection.

Description

technical field [0001] The invention relates to the field of abnormal detection of satellite telemetry data. Background technique [0002] With the development of science and technology, more and more technologies depend on the realization of satellites, so the normal work of satellites is related to many industries and fields; during the operation of satellites in orbit, by monitoring satellite telemetry data, timely discovery of telemetry data Abnormalities play a decisive role in judging that various faults may occur in satellites; [0003] Threshold method is a common method used by the satellite measurement and control center to monitor the abnormality of satellite telemetry data. The main principle is to set the alarm threshold according to the nature and functional requirements of each telemetry data. When the telemetry data value exceeds the alarm threshold, it is judged that the parameters are abnormal, which belongs to abnormal point detection, but some satellite ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 彭宇刘大同陈静庞景月彭喜元
Owner HARBIN INST OF TECH
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