An Analytical Investigation of Anomaly Detection Methods Based on Sequence to Sequence Model in Satellite Power Subsystem

Sensors

Bo Sun¹  Zhidong Li¹  Weihua Jin²  Shijie Zhang²  Pengli Jin³ 

[1] Beijing Institute of Spacecraft System Engineering, Beijing 100094, China;Research Center of Satellite Technology, Harbin Institute of Technology, Harbin 150080, China;School of Materials Science & Engineering, Harbin Institute of Technology, Harbin 150080, China;

关键词: satellite power subsystem;    anomaly detection;    sequence to sequence;   

DOI  :  10.3390/s22051819

来源: DOAJ

【 摘 要 】

The satellite power subsystem is responsible for all power supply in a satellite, and is an important component of it. The system’s performance has a direct impact on the operations of other systems as well as the satellite’s lifespan. Sequence to sequence (seq2seq) learning has recently advanced, gaining even more power in evaluating complicated and large-scale data. The potential of the seq2seq model in detecting anomalies in the satellite power subsystem is investigated in this work. A seq2seq-based scheme is given, with a thorough comparison of different neural-network cell types and levels of data smoothness. Three specific approaches were created to evaluate the seq2seq model performance, taking into account the unsupervised learning mechanism. The findings reveal that a CNN-based seq2seq with attention model under suitable data-smoothing conditions has a better ability to detect anomalies in the satellite power subsystem.

【 授权许可】

Unknown