基于EMD和有向因子图的航天器故障诊断  

Spacecraft fault diagnosis based on empirical mode decomposition and directed factor graph

作　　者：沈毅[1] 张筱磊[1] 王振华[1] 

机构地区：[1]哈尔滨工业大学航天学院,哈尔滨150001

出　　处：《哈尔滨工业大学学报》2013年第1期19-24,共6页Journal of Harbin Institute of Technology

基　　金：国家自然科学基金资助项目(60874054)

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.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.

关 键 词：故障诊断 经验模态分解 有向因子图 特征提取 故障推理 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]