Predictive Maintenance of Manned Spacecraft Through Remaining Useful Life Estimation Technique

被引:0
作者
CHEN Runfeng [1 ]
YANG Hong [1 ]
机构
[1] Institute of Manned Space System Engineering
关键词
remaining useful life; predictive maintenance; Chinese space station;
D O I
暂无
中图分类号
V467 [航天器的维护与修理];
学科分类号
082503 ;
摘要
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.
引用
收藏
页码:3 / 10
页数:8
相关论文
共 50 条
  • [21] Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics
    Lee, Juseong
    Mitici, Mihaela
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2023, 230
  • [22] Remaining useful life estimation: review
    Ahmadzadeh F.
    Lundberg J.
    Ahmadzadeh, Farzaneh, 1600, Springer (05): : 461 - 474
  • [23] Large Scale Predictive Analytics for Hard Disk Remaining Useful Life Estimation
    Anantharaman, Preethi
    Qiao, Mu
    Jadav, Divyesh
    2018 IEEE INTERNATIONAL CONGRESS ON BIG DATA (IEEE BIGDATA CONGRESS), 2018, : 251 - 254
  • [24] Remaining useful lifetime prediction for predictive maintenance in manufacturing
    Tasci, Bernar
    Omar, Ammar
    Ayvaz, Serkan
    COMPUTERS & INDUSTRIAL ENGINEERING, 2023, 184
  • [25] Lifetime Improvement With Predictive Maintenance of Power Electronics Based on Remaining Useful Life Prediction
    Jha, Biplov
    Dong, Lin
    2024 IEEE TEXAS POWER AND ENERGY CONFERENCE, TPEC, 2024, : 327 - 332
  • [26] Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction
    Zhang, Ming
    Amaitik, Nasser
    Wang, Zezhong
    Xu, Yuchun
    Maisuradze, Alexander
    Peschl, Michael
    Tzovaras, Dimitrios
    APPLIED SCIENCES-BASEL, 2022, 12 (07):
  • [27] Using transformer and a reweighting technique to develop a remaining useful life estimation method for turbofan engines
    Kim, Gyeongho
    Choi, Jae Gyeong
    Lim, Sunghoon
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 133
  • [28] Dynamic predictive maintenance strategy for system remaining useful life prediction via deep learning ensemble method
    Wang, Lubing
    Zhu, Zhengbo
    Zhao, Xufeng
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2024, 245
  • [29] Data-driven predictive maintenance strategy considering the uncertainty in remaining useful life prediction
    Chen, Chuang
    Shi, Jiantao
    Lu, Ningyun
    Zhu, Zheng Hong
    Jiang, Bin
    NEUROCOMPUTING, 2022, 494 : 79 - 88
  • [30] Context Driven Remaining Useful Life Estimation
    Johansson, Carl-Anders
    Simon, Victor
    Galar, Diego
    PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE IN THROUGH-LIFE ENGINEERING SERVICES, 2014, 22 : 181 - 185