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

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.