Spacecraft Fault Diagnosis System Based on the Hybrid Intelligence

A hybrid intelligent algorithm with expert system(ES) combining case-based reasoning(CBR) and fault tree(FT) for the diagnosis of spacecraft measurement and control management is studied.FT with bi-direction combination reasoning mechanism is set up to realize spacecraft fault location and prediction.CBR optimized by k-nearest neighbor(KNN) method using multi-sense swarm algorithm(MSA),which is applicable and easy to converge,is constructed.Spacecraft ES combining CBR and FT(SESCF) runs under two kinds of models.Stand-alone model combines CBR with FT,while Loose-Coupling model combines ES with CBR and FT.Considering the improvement of reasoning efficiency,a non-linear transformation merging with special reasoning for changing remote sensing information to semantic data is realized.The fault diagnosis generated by SESCF has been verified using the experiment results of a satellite's electric supply and distribution sub-systems.It is also proved that SESCF is more accurate and reliable comparing with traditional ES.Application of the non-linear transformation to SESCF shows feasibility and good redundancy in spacecraft fault diagnosis