ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Telemetry anomaly detection method based on joint dictionary learning and OCSVM
Received date: 2022-08-18
Revised date: 2022-09-05
Accepted date: 2022-11-28
Online published: 2022-12-06
Supported by
National Natural Science Foundation of China(72071208);Science and Technology Innovation Program of Hunan Province(2020RC4046)
To solve the problems of small fault sample size in spacecraft telemetry data and lack of effective detection methods for correlation anomalies of multi-parameters， Dictionary Learning （DL） is introduced to improve the recognition performance of One-Class Support Vector Machine （OCSVM）. A jointly optimized mechanism is proposed to improve the detection effect and generalization of the model. Firstly， the joint learning function of dictionary learning and OCSVM is given based on telemetry data in the normal state. The optimal dictionary for extracting correlation features and the decision boundary of anomalies are obtained by iterative optimization. Then， test samples are sparsely decomposed via the optimal dictionary， and the extracted features are input into the optimized OCSVM. Finally， anomalies are labeled based on the decision boundary. The proposed method is applied to the MSL dataset published by NASA and telemetry data of a real satellite antenna. The results show that compared with existing one class classification anomaly detection methods， the proposed method shows improved performance in detection precision， F1-score and G-mean， especially in detection of correlation anomalies.
Jiahui HE , Zhijun CHENG , Bo GUO . Telemetry anomaly detection method based on joint dictionary learning and OCSVM[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(13) : 327931 -327931 . DOI: 10.7527/S1000-6893.2022.27931
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