@inproceedings{35f98ba1d390495d87d5b085172dd0b0,
title = "Analytical Investigation of Anomaly Detection Methods based on Time-Domain Features and Autoencoders in Satellite Power Subsystem",
abstract = "This study investigates the potential of autoencoders in detecting anomalies in satellite power subsystem telemetry data. An autoencoder-based ensemble method is proposed while providing a comprehensive comparison on different autoencoder types and time-domain features. Considering the unique learning mechanism of autoencoders, two specific methods have been designed to evaluate the autoencoder performance. The research results show that the combination of kurtosis feature and convolutional autoencoder has stronger detection ability for satellite power subsystem.",
keywords = "anomaly detection, autoencoders, satellite power subsystem, time-domains features",
author = "Weihua Jin and Bo Sun and Zhidong Li and Lei Zhang and Shijie Zhang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 8th International Conference on Dependable Systems and Their Applications, DSA 2021 ; Conference date: 11-09-2021 Through 12-09-2021",
year = "2021",
doi = "10.1109/DSA52907.2021.00068",
language = "英语",
series = "Proceedings - 2021 8th International Conference on Dependable Systems and Their Applications, DSA 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "451--460",
booktitle = "Proceedings - 2021 8th International Conference on Dependable Systems and Their Applications, DSA 2021",
address = "美国",
}