科技创新与应用2024，Vol.14Issue(26)：151-155,5.DOI:10.19981/j.CN23-1581/G3.2024.26.033
基于改进BP神经网络的轨道电路故障预测方法研究
李博文1
作者信息
- 1. 中国铁路武汉局集团有限公司武汉电务段,武汉 430000
- 折叠
摘要
Abstract
ZPW-2000A is an important part of railway signal field and plays an important role in ensuring the safe operation of trains.In order to better predict the failure probability of the track circuit,this paper proposes an improved BP neural network algorithm to predict the fault of the track circuit.The dragonfly algorithm is used to optimize the weight and threshold of the initial BP neural network.Combined with the track voltage data collected in the power workshop,the improved BP neural network is trained,and the voltage values of the track circuits 1 and 2 are predicted and analyzed.The probability and trend of red band in track circuit are obtained.At the same time,the improved BP neural network model is compared with the existing prediction model,and the simulation results show that the improved BP neural network model can predict the track circuit fault probability more accurately and improve the safety and reliability of the equipment.关键词
轨道电路/红光带/故障预测/蜻蜓算法/BP神经网络Key words
track circuit/red band/fault prediction/dragonfly algorithm/BP neural network分类
信息技术与安全科学引用本文复制引用
李博文..基于改进BP神经网络的轨道电路故障预测方法研究[J].科技创新与应用,2024,14(26):151-155,5.
