Research on Knowledge Mining Algorithm of Spacecraft Fault Diagnosis System

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 571)


The change of telemetry data of spacecraft is usually caused by tele-command or fault, which conforms to the causality model of remote-control input and telemetry output under different conditions of spacecraft. Traditional expert system relies on static knowledge of experts to diagnose telemetry parameters. In order to solve the problem of rule-based expert system knowledge acquisition and less manual intervention, considering the characteristics of spacecraft telemetry, this paper proposes an expert knowledge acquisition algorithm based on successful data envelope line and conditional probability from two dimensions of analog and digital quantities respectively. Through data mining of historical telemetry, this algorithm achieves the threshold of analogue quantities and automatic extraction of causal rules at different stages of product life cycle. The experimental results show that the algorithm is effective and the simulation value is more accurate than the product design index and the redundancy of causal rules is less. After knowledge mapping, the algorithm can be applied in the spacecraft fault diagnosis expert system.


Data mining Knowledge acquisition Causal rule 


  1. 1.
    Chao Y (2006) A discussion on the future of data-mining based on the space data-analysis 18(05): 1–2Google Scholar
  2. 2.
    Li Q, Zhou GS (2011) Spacecraft fault diagnosis technology based on measurement and control data mining. Comput Measur Control 19(3):1671–4598MathSciNetGoogle Scholar
  3. 3.
    Giarratano JC, Riley GD (2006) Expert system principles and programming, 4th ednGoogle Scholar
  4. 4.
    Zhang YD, Wu LN, Wang SH (2010) Survey on development of expert system. Comput Eng Appl 46(19):43–47Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Institute of Manned Space System EngineeringBeijingChina

Personalised recommendations