Spacecraft Anomaly Detection with Attention Temporal Convolution Network

• URL: http://arxiv.org/abs/2303.06879v1
• Date: Mon, 13 Mar 2023 05:54:01 GMT
• Title: Spacecraft Anomaly Detection with Attention Temporal Convolution Network
• Authors: Liang Liu and Ling Tian and Zhao Kang and Tianqi Wan
• Abstract summary: We propose an anomaly detection framework for spacecraft time-series data based on temporal convolution networks (TCNs) First, we employ dynamic graph attention to model the complex correlation among variables and time series. Second, temporal convolution networks with parallel processing ability are used to extract multidimensional textcolorbluefeatures for textcolorbluethe downstream prediction task.
• Score: 19.127630728690534
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Spacecraft faces various situations when carrying out exploration missions in complex space, thus monitoring the anomaly status of spacecraft is crucial to the development of \textcolor{blue}{the} aerospace industry. The time series telemetry data generated by on-orbit spacecraft \textcolor{blue}{contains} important information about the status of spacecraft. However, traditional domain knowledge-based spacecraft anomaly detection methods are not effective due to high dimensionality and complex correlation among variables. In this work, we propose an anomaly detection framework for spacecraft multivariate time-series data based on temporal convolution networks (TCNs). First, we employ dynamic graph attention to model the complex correlation among variables and time series. Second, temporal convolution networks with parallel processing ability are used to extract multidimensional \textcolor{blue}{features} for \textcolor{blue}{the} downstream prediction task. Finally, many potential anomalies are detected by the best threshold. Experiments on real NASA SMAP/MSL spacecraft datasets show the superiority of our proposed model with respect to state-of-the-art methods.

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