FSDG在测控系统下行信道中的诊断应用

针对纯定性SDG（Signed Directed Graph,符号有向图）模型的诊断方法因定量知识的缺失导致虚假解多、分辨率低的问题,将SDG定性模型与定量知识相结合,通过在定性模型中加入模糊隶属度与模糊相容度等定量知识,形成了定性定量集成模式的FSDG（Fussy Signed Directed Graph,模糊符号有向图）深层知识模型;并在此基础上,提出一种基于定性定量知识集成模式的故障诊断方法;最后应用该方法建立了某测站下行信道系统的FSDG诊断模型,并进行了故障诊断仿真.结果表明：该方法有效提高了诊断分辨率,具有诊断结果完备、准确性高的优点.

Application of FSDG in Fault Diagnosis of the Downlink Channel of TT＆C Systems

Pure qualitative SDG （Signed Directed Graph） model lacks quantitative data and always has numerous false solutions in the reasoning process with low resolution. To solve the problem, a new half quantitative knowledge model FSDG （Fussy Signed Directed Graph） is proposed by integrating fuzzy membership degree and fuzzy compati- bility into SDG qualitative model. Following this, a fault diagnosis method based on integrated qualitative-quantita- tive knowledge model is constructed. The method is used to develop FSDG diagnosis model for the downlink channel of a TT＆C （Telemetry, Tracking and Command） station. Simulation diagnosis results show that the method im- proves diagnosis resolution and provides more integral and accurate diagnosis results.