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Detecting Anomalies of Satellite Power Subsystem via Stage-Training Denoising Autoencoders

Research Center of Satellite Technology, Harbin Institute of Technology, Harbin 150080, China
Beijing Institute of Spacecraft System Engineering, Beijing 100094, China
Authors to whom correspondence should be addressed.
Sensors 2019, 19(14), 3216;
Received: 1 June 2019 / Revised: 18 July 2019 / Accepted: 19 July 2019 / Published: 22 July 2019
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
PDF [2671 KB, uploaded 22 July 2019]


Satellite telemetry data contains satellite status information, and ground-monitoring personnel need to promptly detect satellite anomalies from these data. This paper takes the satellite power subsystem as an example and presents a reliable anomaly detection method. Due to the lack of abnormal data, the autoencoder is a powerful method for unsupervised anomaly detection. This study proposes a novel stage-training denoising autoencoder (ST-DAE) that trains the features, in stages. This novel method has better reconstruction capabilities in comparison to common autoencoders, sparse autoencoders, and denoising autoencoders. Meanwhile, a cluster-based anomaly threshold determination method is proposed. In this study, specific methods were designed to evaluate the autoencoder performance in three perspectives. Experiments were carried out on real satellite telemetry data, and the results showed that the proposed ST-DAE generally outperformed the autoencoders, in comparison. View Full-Text
Keywords: satellite power subsystem; anomaly detection; stage-training denoising autoencoder satellite power subsystem; anomaly detection; stage-training denoising autoencoder

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Jin, W.; Sun, B.; Li, Z.; Zhang, S.; Chen, Z. Detecting Anomalies of Satellite Power Subsystem via Stage-Training Denoising Autoencoders. Sensors 2019, 19, 3216.

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