TY - GEN
T1 - A Method for On-Orbit Anomaly Detection of Spacecraft Based on SVR and Clustering
AU - Che, Shangjie
AU - Wang, Yalong
AU - Yi, Xiaojian
N1 - Publisher Copyright:
© Beijing HIWING Scientific and Technological Information Institute 2025.
PY - 2025
Y1 - 2025
N2 - Anomaly detection is a crucial aspect of ensuring the on-orbit reliability of spacecraft. By identifying anomalies in telemetry data, the accuracy and reliability of spacecraft operations can be significantly improved. This paper considers both detection accuracy and timeliness to design a method for predicting the behavior of telemetry data based on Granger causality and support vector regression (SVR) models, combined with a clustering approach to achieve adaptive anomaly detection. First, Granger causality modeling is used to explore the causal relationships in the telemetry data. Based on the resulting causality graph, the SVR model is trained using data processed by principal component analysis (PCA) to obtain the prediction errors compared to the original data. Next, a preliminary threshold is determined using the mean squared error (MSE) to filter samples that may contain anomalies. Finally, clustering methods are applied to the errors to calculate the center and standard deviation of each cluster. Based on the clustering results, statistical measures such as skewness, kurtosis, and quantiles are adaptively used to select the anomaly threshold, further optimizing the anomaly detection performance. Experimental results indicate that the proposed method outperforms preset threshold and expert system-based methods in terms of accuracy and adaptability, reducing the false alarm rate by 78.7% and the miss alarm rate by 69.1%. This study’s method is applicable to various data types and scenarios, providing an effective reference for anomaly detection.
AB - Anomaly detection is a crucial aspect of ensuring the on-orbit reliability of spacecraft. By identifying anomalies in telemetry data, the accuracy and reliability of spacecraft operations can be significantly improved. This paper considers both detection accuracy and timeliness to design a method for predicting the behavior of telemetry data based on Granger causality and support vector regression (SVR) models, combined with a clustering approach to achieve adaptive anomaly detection. First, Granger causality modeling is used to explore the causal relationships in the telemetry data. Based on the resulting causality graph, the SVR model is trained using data processed by principal component analysis (PCA) to obtain the prediction errors compared to the original data. Next, a preliminary threshold is determined using the mean squared error (MSE) to filter samples that may contain anomalies. Finally, clustering methods are applied to the errors to calculate the center and standard deviation of each cluster. Based on the clustering results, statistical measures such as skewness, kurtosis, and quantiles are adaptively used to select the anomaly threshold, further optimizing the anomaly detection performance. Experimental results indicate that the proposed method outperforms preset threshold and expert system-based methods in terms of accuracy and adaptability, reducing the false alarm rate by 78.7% and the miss alarm rate by 69.1%. This study’s method is applicable to various data types and scenarios, providing an effective reference for anomaly detection.
KW - adaptive detection
KW - anomaly detection
KW - clustering methods
KW - Granger causality
KW - spacecraft reliability
KW - support vector regression
KW - telemetry data
UR - http://www.scopus.com/inward/record.url?scp=105002454998&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-3592-4_38
DO - 10.1007/978-981-96-3592-4_38
M3 - Conference contribution
AN - SCOPUS:105002454998
SN - 9789819635917
T3 - Lecture Notes in Electrical Engineering
SP - 367
EP - 376
BT - Proceedings of 4th 2024 International Conference on Autonomous Unmanned Systems, 4th ICAUS 2024 - Volume VII
A2 - Liu, Lianqing
A2 - Niu, Yifeng
A2 - Fu, Wenxing
A2 - Qu, Yi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Autonomous Unmanned Systems, ICAUS 2024
Y2 - 19 September 2024 through 21 September 2024
ER -