基于自适应遗传优化神经网络的航空装备故障诊断

Fault diagnosis of aviation equipment based on adaptive genetic optimization neural network

针对改进反向传播神经网络在航空装备故障诊断中存在的缺陷和不足,将自适应遗传算法与改进反向传播算法相结合构成混合算法用以训练人工神经网络。以改进反向传播神经网络的初始权值空间为切入点,利用改进遗传操作对其开展多点自适应遗传优化,然后运用改进反向传播算法开展局部精确搜索,最终实现全局最优。以某型飞机电气控制盒和某型飞机自动驾驶仪飞行控制盒的故障诊断为例对所提算法进行仿真研究,结果表明自适应遗传算法与改进反向传播算法相结合的方法收敛速度快、诊断精度高,对于具有复杂输入输出关系的工程样本具有较好的诊断结果。

In response to the shortcomings and deficiencies of the improved back propagation network in aviation equipment fault diagnosis,a hybrid algorithm is formed by combining the adaptive genetic algorithm and the improved back propagation algorithm to train an artificial neural network.Taking the improvement of the initial weight space of the back propagation network as the starting point,a multi-point adaptive genetic optimization is carried out using the improved genetic operation.Based on this,the improved back propagation algorithm is used to carry out local precise search and ultimately achieve global optimization.Taking the fault diagnosis of a certain aircraft electrical control box and a certain aircraft autopilot flight control box as examples,the proposed algorithm was simulated and studied.The simulation results showed that the combination of adaptive genetic algorithm and improved back propagation algorithm has fast convergence speed and high diagnostic accuracy,and has good diagnostic results for engineering samples with complex input-output relationship.