摘要
为了提高诊断效率,降低故障诊断对人的过分依赖,建立了一个航空发动机故障快速诊断模型。该模型结合粗糙集和灰色理论各自特点,利用粗糙集去除特征信息中的冗余信息;再通过多参数灰色模型对约简后的信息进行快速准确的预测。仿真对比表明,该模型在不降低预测精度的情况下能显著减少计算时间,有效提高故障预测的快速性和实时性。
For the sake of improving efficiency of diagnosis and lowering the dependency of manual labor, this paper proposed a fast fault diagnosis model of aeroengine. The model combined characteristics of rough sets and grey theory. Redundant information was eliminated from feature information by rough sets, and then the reduction information was quickly and accurately predicted by multi-parameter grey model. The simulation results show preliminarily that the model saves the computing time greatly and can effectively meet the requirements of fast and real-time fault prediction while precision is not reduced.
出处
《计算机应用研究》
CSCD
北大核心
2011年第12期4564-4566,4575,共4页
Application Research of Computers
基金
航空科学基金资助项目(2010ZD53039)
陕西省自然科学基金资助项目(2010HQ8005)
