PSO神经网络在光电探测设备故障诊断中的应用

摘要

针对传统神经网络在光电探测故障诊断中存在故障检测率低、诊断时间长的问题,研究了粒子群优化算法( PSO)优化神经网络连接权值,并将其应用于某型光电探测设备的故障诊断中. 实验结果表明,与BP和GA相比,PSO算法更易实现,具有更快的收敛速度、更高的故障检测率、更低的虚警率和更短的故障诊断时间,从而获得了更好的故障诊断效果.%Aiming at the low detection rate and long diagnosis time of the traditional neural network methods, particle swarm optimization ( PSO ) is used to train neural network and optimize connection weights. It is applied to fault diagnosis of optical-electronic detection equipment. Compared to BP and GA, the experiment results show that PSO neural network can improve the fault detection rate, decrease the false alarm rate of optical-electronic detection equipment and fault diagnosis time, and be realized more easily. Therefore, It gets better effects of fault diagnosis.