A satellite anomaly detection method of an adversarial network autoencoder

Abstract

The invention discloses an abnormity detection method for satellite telemetry data through an adversarial network autoencoder, and the method comprises the steps: breaking the limitation of a traditional empirical model, and employing a pure data driving model; on the basis of a variational autoencoder, introducing a confrontation network idea, using a bidirectional LSTM (Long Short Term Memory) (Long-short term memory network) as a discriminator, and judging whether satellite telemetry data is abnormal or not by using errors of reconstructed data and original data; aiming at the redundancy problem of a satellite sensor, the conventional situation is broken through, and a Markov distance is used for measuring a reconstruction error. In combination with periodicity of satellite orbit operation, a dynamic threshold determination method based on a periodic time window is provided. The method has the advantages that pure data driving is adopted, expert experience is not needed, and the method can be suitable for various occasions; By combining the respective advantages of the variational auto-encoder and the generative adversarial network, the proposed network has the characteristics of high training speed and relatively easy convergence; eliminating redundant data influence between satellite telemetry data by adopting a Mahalanobis distance. According to the periodicity of the satellite, the dynamic threshold method based on the periodic time window is provided, and the misjudgment rate is reduced.

Description

technical field [0001] The invention relates to a satellite anomaly detection method against a network self-encoder, which is a method for automatically performing anomaly detection on satellite telemetry data, and belongs to the intersecting field of engineering application and information science. Background technique [0002] Since the satellite is in the harsh outer space environment such as solar radiation for a long time, unpredictable abnormalities or failures may occur during its in-orbit operation. Taking measures in advance to detect these unpredictable abnormalities or failures in time is crucial to ensuring the long-term stable operation of the satellite. important. Therefore, anomaly detection of telemetry data is of great significance in the fields of satellite troubleshooting and real-time health monitoring. Considering the complex design structure and harsh working environment of the satellite, it is impossible to directly perform anomaly detection in the ou...

AI Technical Summary

Problems solved by technology

This method has a high detection accuracy rate for telemetry data with strong periodicity, but for telemetry data with insignificant periodicity or no periodicity, it cannot accurately judge the change of its frequency, so it cannot detect abnormalities

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