Chinese Space Science and Technology ›› 2021, Vol. 41 ›› Issue (3): 97-104.doi: 10.16708/j.cnki.1000.758X.2021.0043

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Fault diagnosis for momentum wheel bearing based on spectral kurtosis entropy and hierarchical extreme learning machine

LIU Luhang1,2,ZHANG Qiang2,WANG Hong2,LI Gang2,WU Hao2,WANG Zhipeng3,GUO Baozhu2,*,ZHANG Jiyang1   

  1. 1 China Aerospace Academy of Systems Science and Engineering, Beijing 100037, China
    2 Beijing Institute of Control Engineering, Beijing 100094,China
    3 State Key Lab of Rail Traffic Control & Safety, Beijing Jiaotong University, Beijing 100044, China
  • Received:2021-02-01 Revised:2021-03-02 Accepted:2021-03-03 Online:2021-06-25 Published:2021-06-25
  • Contact: 郭宝柱:18911899515@163.com E-mail:18911899515@163.com
  • About author:刘鹭航(1984-),男,博士研究生,高级工程师,研究方向为系统工程、健康管理,liuluhang@sina.com。 郭宝柱(1945-),男,博士,教授,研究方向为系统工程,18911899515@163.com。
  • Supported by:
    国家自然科学基金资助项目基金(U1837602);国家自然科学基金资助项目基金(61803022)

Abstract: The momentum wheel is the key component of the satellite attitude control system, and its reliability is directly related to the life and safety of the whole satellite. As the core component of momentum wheel, bearing is prone to failure. Due to its unique structure and complex operating environment, the signal to noise ratio of monitoring signals is low, and early fault diagnosis is difficult. Aiming at this situation, a feature extraction method combining variational mode decomposition and kurtosis entropy was proposed to obtain the weak fault features of momentum wheel bearing monitoring signals and to establish the feature vectors. The layered extreme learning machine was introduced, and the structure and coding method were optimized for bearing fault identification. Finally, the proposed method was applied to the actual fault diagnosis. The comparison with the traditional ELM method shows that the proposed method has higher diagnostic accuracy (98.5%) in the fault diagnosis of momentum wheel bearings.

Key words: fault diagnosis, momentum wheel bearing, variational mode decomposition, spectral kurtosis entropy;hierarchical extreme learning machine

CLC Number: