基于EMD和有向因子图的航天器故障诊断
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(哈尔滨工业大学 航天学院, 150001 哈尔滨)

作者简介:

沈毅(1965—),男,教授,博士生导师.

通讯作者:

沈毅, shen@hit.edu.cn.

中图分类号:

TP273

基金项目:

国家自然科学基金资助项目 (60874054).


Spacecraft fault diagnosis based on empirical mode decomposition and directed factor graph
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(School of Astronautics, Harbin Institute of Technology, 150001 Harbin, China)

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    摘要:

    为了消除噪声对提取传感器信号中故障特征的影响,同时在系统模型不精确条件下,描述故障在系统部件间的传播方式.本文提出了一种基于经验模态分解(EMD)和有向因子图(DFG)的故障诊断方法.对传感器信号进行经验模态分解得到的内部模态函数(IMF),提出采用能量做为其零点区间包含噪声成分的评价指标,基于信号内部模态函数的区块能量消除其噪声成分.对无法精确建模的物理系统,提出使用有向因子图描述系统组成部件间的因果关系,应用概率推理实现故障诊断.通过对航天器电源系统供电模块的实例分析,验证了方法的有效性.

    Abstract:

    To solve the problem of noise elimination in fault feature extraction of sensor signal and describing fault propagation under model uncertainty, this article presents a novel fault diagnosis approach based on empirical mode decomposition (EMD) and directed factor graph (DFG). The EMD method is used to decompose the sensor output signal into a number of intrinsic mode function (IMF) components, a block energy criterion based on the signal samples between two adjacent zero-crossings of IMF is proposed to distinguish the useful signal from noise. Directed factor graph is used to model the cause-effect relations between system components, and as the basis for fault diagnosis through probabilistic reasoning under the model uncertainty. A power supply module of a spacecraft power system is provided as case study to show the feasibility and validity of the proposed method.

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沈毅,张筱磊,王振华.基于EMD和有向因子图的航天器故障诊断[J].哈尔滨工业大学学报,2013,45(1):19. DOI:10.11918/j. issn.0367-6234.2013.01.004

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  • 在线发布日期: 2013-01-28
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