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

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.