A Label-Free Time Series Anomaly Detection Method

A technology of category labeling and time series, applied in the field of time series anomaly detection, can solve the problems of manually setting the number of clusters in hierarchical clustering and unsatisfactory segmentation effect of fixed points of satellite telemetry data, so as to achieve compact and coupled segmentation results. high degree of effect

Active Publication Date: 2018-03-30
HARBIN INST OF TECH
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Problems solved by technology

[0005] The purpose of the present invention is to solve the unsatisfactory effect of segmenting fixed points for satellite telemetry data, the need to manually set the number of clusters for hierarchical clustering, and the fact that there is currently no directly available offline and label-free time series. The problem of online anomaly detection method framework, and propose a time series anomaly detection method without category labels

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  • A Label-Free Time Series Anomaly Detection Method
  • A Label-Free Time Series Anomaly Detection Method
  • A Label-Free Time Series Anomaly Detection Method

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specific Embodiment approach 1

[0036] Specific implementation mode one: combine figure 1To illustrate this embodiment, a time series anomaly detection method without category labels is specifically carried out according to the following steps:

[0037] Step 1. Segment the historical satellite telemetry data according to the periodic characteristics of the satellite telemetry data, and obtain a time series without class labels X={x 1 ,x 2 ,...,x n}, where n is a positive integer greater than 0, indicating the number of time series, x 1 is the first time series in the time series without category labels, x 2 is the second time series in the time series without class labels, x n is the nth time series in the time series without class labels;

[0038] Step 2. For the time series without class labels obtained in step 1, X={x 1 ,x 2 ,...,x n} Carry out adaptive hierarchical clustering, and determine and delete the abnormal sequence in the time series without category labels, and obtain the time series wi...

specific Embodiment approach 2

[0040] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in the step 1, the satellite telemetry historical data is segmented according to the periodic characteristics of the satellite telemetry data, and the time series X={x without category labels is obtained 1 ,x 2 ,...,x n}, where n is a positive integer greater than 0, indicating the number of time series, x 1 is the first time series in the time series without category labels, x 2 is the second time series in the time series without class labels, x n is the nth time series in the time series without class labels; the specific process is: segment the historical data of satellite telemetry with the point of change in argument as the mark, and obtain the time series without class labels X={x 1 ,x 2 ,...,x n}.

[0041] Argument is one of the test parameters in satellite telemetry data. Its value changes in order from 0 to 360. When it reaches 360, it becomes 0 and starts to ...

specific Embodiment approach 3

[0043] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: in the step two, the time series without class labels obtained in step one X={x 1 ,x 2 ,...,x n} Carry out adaptive hierarchical clustering, and determine and delete the abnormal sequence in the time series without category labels, and obtain the time series with category labels under the normal operation mode of the satellite and category labels where n z is a positive integer greater than 0, indicating the number of normal time series, x' 1 is the first normal time series in the time series with category labels, x' 2 is the second normal time series in the time series with class labels, is the nth time series with category labels z a normal time series, l' 1 is the first normal time series in the category label, l' 2 is the second normal time series in the category labels, is the nth in the category label z A normal time series; the specific process i...

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Abstract

A time series anomaly detection method without category label relates to a time series anomaly detection method without category label. The purpose of the present invention is to solve the problem that the fixed point segmentation effect for satellite telemetry data is not ideal, the number of clusters needs to be manually set for hierarchical clustering, and there is currently no directly available offline and label-free time series that can be used. Problems in the framework of online anomaly detection methods. It is achieved by the following technical solutions: step 1, segment the historical satellite telemetry data according to the periodic characteristics of the satellite telemetry data, and obtain a time series X={x1,x2,...,xn} without category labels; step 2, pair the step Once the obtained X={x1,x2,...,xn}, perform adaptive hierarchical clustering, and determine and delete abnormal sequences in the time series without class labels, and obtain the sum; step 3, combine the matching threshold with the sum as the sample, The nearest neighbor algorithm is used to perform pattern matching on x" to realize abnormal detection of satellite telemetry data. The invention is applied to the field of satellite data detection.

Description

technical field [0001] The invention relates to a time series anomaly detection method without category labels. Background technique [0002] By analyzing the yaw attitude angle in the satellite telemetry data, the overall change trend of the yaw attitude angle is as follows: figure 2 As shown, its details change as image 3 As shown, the satellite telemetry data has obvious periodicity, and this characteristic has been confirmed with the satellite telemetry data provider. By analyzing each period of the telemetry data, it can be concluded whether the satellite’s operating status within the period is normal, and the effect of segmenting the satellite telemetry data according to the fixed point is not ideal, such as Figure 4 As shown, the coupling degree between each sub-sequence is not high enough, there is a certain deviation, and this deviation will become more and more obvious as time goes on. [0003] At present, there are no clear reference materials for the normal ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30G06K9/62G06K9/66
CPCG06F16/285G06V30/194G06V2201/10G06F18/22
Inventor 刘大同彭宇陈静张玉杰彭喜元
Owner HARBIN INST OF TECH
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