机载稳定平台伺服系统故障分析及诊断方法研究

On Fault Analysis and Diagnosis of Servo System in Airborne Stabilized Platform

针对在恶劣环境下运行的随动稳定平台伺服系统,采用故障树分析法确定其故障类别和逻辑关系,基于粗糙集理论建立原始决策表,使用可辨识矩阵与遗传算法相结合的方法对其进行约简,以约简后的决策表作为学习样本,训练Elman神经网络后生成故障诊断模型,使用测试样本进行校验,故障诊断正确率达98%,表明了该诊断方法的可行性,对故障模型较为复杂的稳定平台伺服系统的诊断应用具有实践意义。

To the servo system of stabilized platform working in a severe environment,Fault Tree Analysis （FTA） method is used to determine the fault classification and the logical relation. Firstly, based on rough set theory, the original fault decision table is constructed. Discernibility matrix and genetic algorithm are used together for reduction of it. Then, the table after reduction is used as learning samples for training Elman neural network to generate fault diagnosis model. Finally, test samples are used to verify fault diagnosis model. The correct rate of fault diagnosis reaches 98%. It shows that this method is feasible, and it has a certain guiding significance to the fault diagnosis of servo system with complex fault model.