| 基于峭度熵与分层极限学习机的动量轮轴承故障诊断研究 |
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| 引用本文： | 刘鹭航,张强,王虹,李刚,吴昊,王志鹏,郭宝柱,张激扬. 基于峭度熵与分层极限学习机的动量轮轴承故障诊断研究[J]. 中国空间科学技术, 2021, 41(3): 97-104. DOI: 10.16708/j.cnki.1000.758X.2021.0043 |
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| 作者姓名： | 刘鹭航 张强 王虹 李刚 吴昊 王志鹏 郭宝柱 张激扬 |
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| 作者单位： | 1 中国航天系统科学与工程研究院，北京1000372 北京控制工程研究所，北京1000943 北京交通大学轨道交通控制与安全国家重点实验室，北京100044 |
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| 基金项目： | 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） |
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| 摘 要： | 动量轮是卫星姿态控制系统的关键部件,其可靠性直接关系到整星寿命与安全.作为动量轮的核心组件,轴承易于发生故障,且独特结构和复杂运行环境导致监测信号信噪比低,早期故障诊断困难.针对这种情况,对变分模态分解和峭度熵结合的特征提取方法进行研究,获得动量轮轴承监测信号中的微弱故障特征,并建立特征向量.引入分层极限学习机,对结构...
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| 关 键 词： | 故障诊断 动量轮轴承 变分模态分解 峭度熵 分层极限学习机 |
| 收稿时间： | 2021-02-01 |
Fault diagnosis for momentum wheel bearing based on spectral kurtosis entropy and hierarchical extreme learning machine |
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| LIU Luhang,ZHANG Qiang,WANG Hong,LI Gang,WU Hao,WANG Zhipeng,GUO Baozhu,ZHANG Jiyang. Fault diagnosis for momentum wheel bearing based on spectral kurtosis entropy and hierarchical extreme learning machine[J]. Chinese Space Science and Technology, 2021, 41(3): 97-104. DOI: 10.16708/j.cnki.1000.758X.2021.0043 |
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| Authors: | LIU Luhang ZHANG Qiang WANG Hong LI Gang WU Hao WANG Zhipeng GUO Baozhu ZHANG Jiyang |
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| Affiliation: | 1 China Aerospace Academy of Systems Science and Engineering, Beijing 100037, China2 Beijing Institute of Control Engineering, Beijing 100094，China3 State Key Lab of Rail Traffic Control & Safety, Beijing Jiaotong University, Beijing 100044, China |
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| 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. |
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| Keywords: | fault diagnosis momentum wheel bearing variational mode decomposition spectral kurtosis entropy；hierarchical extreme learning machine |
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