Computer Science ›› 2020, Vol. 47 ›› Issue (2): 227-232.doi: 10.11896/jsjkx.190600147

• Computer Network • Previous Articles     Next Articles

Communication Satellite Fault Detection Based on Recurrent Neural Network

LIU Yun1,2,YIN Chuan-huan1,2,HU Di3,ZHAO Tian3,LIANG Yu3   

  1. (School of Computer and Information Technology,Beijing 100044,China)1;
    (Beijing Key Laboratory of Traffic Data Analysis and Mining,Beijing 100044,China)2;
    (China Academy of Space Technology,Beijing 100094,China)3
  • Received:2019-06-26 Online:2020-02-15 Published:2020-03-18
  • About author:LIU Yun,born in 1995,postgraduate.His main research interests include machine learning and anomaly detection;YIN Chuan-huan,born in 1976,associate professor.His main research interests include SVM,machine learning and network security (intrusion detection).
  • Supported by:
    This work was supported by the Fundamental Research Funds for the Central Universities of Ministry of Education of China (2018JBZ006).

Abstract: With the rapid development of modern spaceindustry,the structure of communication satellites is becoming more and more complex,while its faults are gradually increasing,and fault detection of communication satellites has become a key issue in the current aerospace field.At present,the detection of faults by major space agencies is still based on simple upper and lower threshold’s detection.The method is too simple and can only detect a small number of specific faults.Early studies using traditional machine learning for detection can only detect faults in quantitative characteristics.Aiming at the problem that traditional machine learning algorithms are difficult to effectively learn the trend of telemetry data,this paper proposed a thresholding methodbased on long-short-time memory network.LSTM prediction model is used to learn the trend change of the satellite telemetry data,and at the same time to maximize the correlation coefficient and the F1 score,to determine the appropriate threshold for the fault determination of the multi-dimensional telemetry data.This method can effectively judge the fault by the trend of the satellite telemetry data.The experimental data is based on the 24D communication satellite telemetry data provided by a space agency for 2 years.The core model LSTM network is trained on NVIDIA GTX TITAN X.The final model accuracy is 99.34%,the precision is 81.93%,and the recall rate was 94.62%.At the same time,compared with the traditional machine learning algorithm and the LSTM-based non-threshold method,the accuracy of the model is significantly higher.The experimental results show that the LSTM network can efficiently learn the trend characteristics of satellite telemetry data.At the same time,using the appropriate method to select the threshold value,it can effectively detect the faultsof the communication satellite which successfully solve the problem of communication satellite fault detection in the aerospace field.

Key words: Telemetry data, Fault detection, Machine learning, Long short-term memory Network, Thresholding

CLC Number: 

  • TP181
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