宇航计测技术 ›› 2015, Vol. 35 ›› Issue (6): 86-91.doi: 10.12060/j.issn.1000-7202.2015.06.19

• 论文 • 上一篇    

PSO神经网络在光电探测设备故障诊断中的应用

邓俊1；周越文1；杨召1；梁鹏2；张鸣鸣3   

  1. 1、空军工程大学 航空航天工程学院，西安 710038；
    2、95503部队，重庆 402360；
    3、中国人民解放军驻航宇救生装备有限公司军事代表室，襄阳 441000
  • 出版日期:2015-12-15 发布日期:2015-12-15
  • 作者简介:邓俊（1991-），男，硕士研究生，主要研究方向：光电探测设备故障诊断、测试方法研究。

Application of PSO Neural Network for Fault Diagnosis of Optical-electronic Detection Equipment

DENG Jun1；ZHOU Yue-wen1；YANG Zhao1；LIANG Peng2；ZHANG Ming-ming3   

  1. 1、Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi′an 710038;
    2、95503 Unit of PLA,Chongqing 402360; 
    3、Military Representative Office of PLA in Hangyu Lifesaving Equipment Co.Ltd, Xiangyang 441000
  • Online:2015-12-15 Published:2015-12-15

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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。

关键词: 反向传播算法, 粒子群优化, 遗传算法, 神经网络, 光电探测, 故障诊断

Abstract: 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.

Key words: Back propagation (BP), Particle swarm optimization (PSO), Genetic algorithm (GA), Neutral network, Optical-electronic detection, Fault diagnosis

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