基于概率神经网络的ZPW-2000轨道电路衰减器性能分类

Minggui Huang
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引用次数: 0

摘要

ZPW-2000型非绝缘移频轨道电路设备在日常维护工作中需要手工测试电气参数,导致现场维护人员劳动强度高,工作效率低。本文设计的智能ZPW-2000轨道电路衰减器可以实时监控和显示轨道电路的相关参数和设备状态,使维修人员无需繁琐的操作就可以直接查看设备的信息,提高了维修效率和设备的可靠性。确保铁路运行安全。轨道电路的故障诊断对铁路运营的安全运行具有重要意义。首先利用轨道电路佩戴者采集参数,根据已有的维护经验将数据分为5类。采用概率故障神经网络(PNN)对轨道电路的性能分类进行诊断。实验结果表明,正确率达到95%以上,取得了较好的效果,为铁路道岔系统的维护和道岔性能的提高提供了理论依据和实践经验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Class Classification of ZPW-2000 Track Circuit Attenuator Based on Probabilistic Neural Network
ZPW-2000 uninsulated frequency-shifted rail circuit equipment requires manual testing of electrical parameters in daily maintenance work, resulting in high labor intensity and low work efficiency of maintenance personnel in the field. The intelligent ZPW-2000 track circuit attenuator designed in this article can monitor and display the relevant parameters and equipment status of the track circuit in real time, so that maintenance personnel can directly view the information of the equipment without tedious operation, which can improve maintenance efficiency and equipment reliability. Safety of railroad operation can be ensured. Fault diagnosis of rail circuits is of great importance for the safe operation of railroad operations. First of all, the parameters are collected by using the track circuit wearer and the data are divided into 5 classes according to the existing maintenance experience. The performance classes of the track circuits were diagnosed using probabilistic fault neural networks (PNN). The experimental results show that the correct rate reaches more than 95% and achieves good results, which provides theoretical basis and practical experience for the maintenance of railroad turnout system and improvement of turnout performance.
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