AEROSPACE CHINA

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China Aerospace Academy of Systems Science and Engineering

ISSN 1671-0940

CN 11-4673/V

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AEROSPACE CHINA ›› 2018, Vol. 19 ›› Issue (3): 3-10.doi: 10.3969/j.issn.1671-0940.2018.03.001

• Research Articles •     Next Articles

Predictive Maintenance of Manned Spacecraft Through Remaining Useful Life Estimation Technique

  

  1. Institute of Manned Space System Engineering, Beijing 100094
  • Online:2018-08-24 Published:2019-11-29
  • About author:CHEN Runfeng (1989.12- ), received the M.Eng. degree in Computing Science and Technology from Liaoning University, Shenyang, China, in 2013. Since 2013, he has been working towards his Ph.D. degree in the Institute of Manned Space System Engineering, China Academy of Space Technology (CAST), Beijing, China. His research interests include prognostics and health management, time series data analysis, and artificial intelligence.

Abstract: Manned spacecraft pose challenges in terms of extremely high safety and reliability, and with the growth of system complexity and longer on-orbit operation time, the traditional management mode, such as monitoring the threshold of parameter passively, is difficult to meet the required safety standards. Predictive maintenance, which analyzes the system heath trend and estimates remaining useful life (RUL) to establish maintenance strategies ahead of time before failure occurs, is a new mode to approach maintenance tasks. Here, a predictive maintenance strategy for complex manned spacecraft is proposed based on the remaining useful life estimation technique. Firstly, a health index is established based on an abundance of telemetry data, reflecting the system’s current health state. Secondly, we map the health index to the remaining useful life through system degradation modelling, taking into consideration both the 
system’s stochastic deterioration and uncertainty. The maintenance and management strategies are then made based on the calculated distribution of RUL time. Finally, a case study on Chinese space station energy system predictive maintenance is presented.