Intelligent Fault Diagnosis of Military Power Based on BP Neural Network

DOI

10.2991/icmmcce-17.2017.144How to use a DOI?

Keywords

neural network; three phase DC/AC inverter; fault diagnosis

Abstract

The reliable operation of power equipment related to the performance of surface-to-air missile weapon systems, accurate fault diagnosis of power system is very important. In order to make an accurate diagnosis for power supply equipment of the surface-to-air missile weapon systems, this paper introduces the BP neural network and the related knowledge of some type missile static variable power supply. The fault model of three phase DC/AC inverter is established, and several common faults are analyzed briefly. BP neural model is applied to the fault diagnosis of a certain type of ground to air missile static variable power, the neural network's ability to classify the pattern is good, solve the previous static inverter fault diagnosis problems 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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Rui Zhang
AU  - Bo Fan
AU  - Xinyu Luan
PY  - 2017/09
DA  - 2017/09
TI  - Intelligent Fault Diagnosis of Military Power Based on BP Neural Network
BT  - Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017)
PB  - Atlantis Press
SP  - 799
EP  - 804
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmcce-17.2017.144
DO  - 10.2991/icmmcce-17.2017.144
ID  - Zhang2017/09
ER  -