An Adaptive Amplitude Geometric Mapping Method for Typical Telemetry Anomaly Detection and Localization

Abstract

The invention discloses an adaptive amplitude geometric mapping method for typical telemetry anomaly detection and positioning, which includes: obtaining the multi-period telemetry frame count sequence of the spacecraft in a continuous time period; The frame count value sequence is mapped to obtain the first set A and the second set B; through the clustering algorithm, the elements in the first set A and the second set B are respectively clustered to analyze the discreteness of each element Degree; telemetry anomaly detection based on how discrete each element is.

Description

technical field [0001] The invention relates to a telemetry detection method, in particular to an adaptive amplitude geometric mapping method for typical telemetry anomaly detection and positioning, and belongs to a spacecraft telemetry anomaly detection method. Background technique [0002] With the development of science and technology, more and more technologies depend on the realization of spacecraft, and the normal operation of spacecraft is related to many industries and fields; during the operation of the spacecraft in orbit, the telemetry data downloaded by the spacecraft is the state of the system. The direct observation of the spacecraft can reflect the function, performance and working status of the equipment on the spacecraft. By monitoring the telemetry data of the spacecraft, timely detection of abnormal telemetry data plays a decisive role in judging that various failures of the spacecraft may occur. [0003] The spacecraft telemetry frame count has a typical ...

AI Technical Summary

Problems solved by technology

[0004] At present, the anomaly detection of spacecraft telemetry data usually adopts manual data interpretation or based on expert system methods. These methods are simple and easy to implement and easy to implement, but they all have some obvious limitations. For example, these methods cannot detect abnormalities or unknown patterns, it is difficult to meet the needs of complex system telemetry data anomaly detection

Therefore, the data-driven anomaly detection method has been widely promoted. This method can reduce the dependence on expert experience, but it still has problems such as difficult parameter setting, low precision, and poor stability.

[0005] In particular, for the sequentially increasing cyclic frame data structure of the spacecraft telemetry frame count, the previous data-driven method has complex calculations, involves many judgment conditions, and has low accuracy. It relies too much on training data and cannot distinguish system faults, especially Based on a purely data-driven approach, the design knowledge of the spacecraft itself and the characteristics of the satellite telemetry data itself will be ignored

Images