遗传算法和粗糙集相结合的航空电子系统故障诊断方法研究

An Effective Method for Diagnosing Fault of Avionics System

提出了一种将遗传算法和粗糙集理论相结合的航空电子系统故障诊断方法。该方法运用遗传算法对系统输出的连续数据进行离散化,运用粗糙集理论提取知识规则,用得到的规则进行系统的故障诊断。同时给出了一种运用粗糙集理论增减规则库中规则的方法,以便快速提取新样本知识。仿真实验和某型航空电子系统的实际应用表明:该方法能够有效诊断系统故障且诊断效率高。

Our method is very effective because it combines the advantages inherent In GA（Genetic Algorithm） and rough set theory. In the full paper, we explain our method in much detail; here we give only a briefing. We discretize the measured data by using GA and diagnose the system fault by means of knowledge discovered by rough set theory. We present two algorithms to discover knowledge： one is to discover knowledge and the other is to update the discovered knowledge. We took as numerical example the avionics system of a certain fighter aircraft. There were many faults of this avionics system that were difficult to diagnose by existing methods. We applied our method to 500 such difficult-to-diagnose faults and diagnosed 493 faults correctly. This shows preliminarily that our method is very effective as the correct diagnosis rate is as high as 98.6 %.