Volume 40 Issue 1
Jan.  2020
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LI Yukui, LI Hu, HU Tai. In-orbit Operational Pattern Monitoring Algorithms Based on LightGBM for Hard X-ray Modulation Telescope Satellite[J]. Journal of Space Science, 2020, 40(1): 109-116. doi: 10.11728/cjss2020.01.109
Citation: LI Yukui, LI Hu, HU Tai. In-orbit Operational Pattern Monitoring Algorithms Based on LightGBM for Hard X-ray Modulation Telescope Satellite[J]. Journal of Space Science, 2020, 40(1): 109-116. doi: 10.11728/cjss2020.01.109

In-orbit Operational Pattern Monitoring Algorithms Based on LightGBM for Hard X-ray Modulation Telescope Satellite

doi: 10.11728/cjss2020.01.109
  • Received Date: 2019-01-10
  • Rev Recd Date: 2019-06-12
  • Publish Date: 2020-01-15
  • The frequent switching of space hard X-ray sky survey and fixed-point observation schemes of HXMT (Hard X-ray Modulation Telescope) satellite requires real-time monitoring and identification for the satellite payloads in-orbit status. Manual monitoring according to the rules summarized by experts are used at present. Although the manual monitoring method is easy to execute and explicable, it consumes a lot of manpower and can not deal with the situation outside the rules flexibly. According to the real-time telemetry data of HXMT satellite, an in-orbit operation mode monitoring algorithm based on LightGBM is proposed in this paper. The in-orbit operational pattern monitoring is reduced to a multi-classification problem, and a discriminant model is constructed to efficiently judge the in-orbit operation mode of the satellite. On the premise of ensuring the accuracy of discrimination, the algorithm model is constructed very quickly, which liberates the monitoring personnel from the heavy rule judgment work and has high practicability. The experiments based on the real telemetry data show that the accuracy rate of the model is 99.9%, which can meet the requirement of in-orbit operation mode monitoring, and can provide references for HXMT satellite operation monitoring task.

     

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  • [1]
    CHEN Wei, HU Guangrui, WANG Yaping. Knowledge acquisition in an expert system for fault diagnosis of certain spacecraft[J]. J. Shanghai Jiaotong Univ., 2000, 34(6):845-847(陈玮, 胡光锐, 汪亚平.飞行器故障诊断专家系统中的知识获取机制[J]. 上海交通大学学报, 2000, 34(6):845-847)
    [2]
    KRIZHEVSKY A, SUTSKEVER I, HINTON G. Imagenet classification with deep convolutional neural networks[C]//Neural Information Processing Systems. Lake Tahoe:NIPS, 2012:1106-1114
    [3]
    XIE Lun, LIU Fan, GONG Xiao, et al. Spacecraft fault diagnosis system based on the hybrid intelligence[J]. Inf. Control, 2010, 39(1):106-113(解仑, 刘帆, 巩潇, 等. 基于混合智能的航天器故障诊断系统[J]. 信息与控制, 2010, 39(1):106-113)
    [4]
    BALDI P, BLANKE M, CASTALDI P, et al. Combined geometric and neural network approach to generic fault diagnosis in satellite reaction wheels[J]. IFAC, 2015, 48(21):194-199
    [5]
    KE G, MENG Q, FINLEY T, et al. LightGBM:a highly efficient gradient boosting decision tree[C]//Neural Information Processing Systems. Long Beach:NIPS, 2017:3146-3154
    [6]
    FRIEDMAN J H. Stochastic gradient boosting[J]. Comput. Stat. Data An., 2002, 38(4):367-378
    [7]
    CHEN T, GUESTRIN C. XGBoost:a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco:ACM, 2016:785-794
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