测控技术2017，Vol.36Issue(5)：25-28,4.
基于BP神经网络的军用电源智能故障诊断
Intelligent Fault Diagnosis of Military Power Based on BP Neural Networks
张瑞 1樊波 1牛天林 1赵广胜1
作者信息
- 1. 空军工程大学防空反导学院,陕西西安710051
- 折叠
摘要
Abstract
The reliable operation of power equipment is related to the performance of surface-to-air missile weapon systems,accurate fault diagnosis is very important for power system.In order to accurately diagnose the power supply equipment of the surface-to-air missile weapon systems,the BP neural networks and the related knowledge of a certain type missile static variable power supply are introduced.The fault model of the three phase DC/AC inverter is established,and several conmon faults are analyzed briefly.The BP neural model is applied to the fault diagnosis of a certain type of surface-to-air missile static variable power,better pattern classification capability of the neural network is used to solve the previous static inverter fault diagnosis problem of surface-to-air missile troops.The simulation results show that the method can diagnose the fault of power equipment accurately,and the accuracy and practicability of the method are verified.关键词
神经网络/三相DC/AC逆变器/故障诊断Key words
neural networks/three phase DC/AC inverter/fault diagnosis分类
信息技术与安全科学引用本文复制引用
张瑞,樊波,牛天林,赵广胜..基于BP神经网络的军用电源智能故障诊断[J].测控技术,2017,36(5):25-28,4.
