Research on Online Fault Warning Method for EMU MOA

ZHU Wenlong; LI Qiang; XU Hailong

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Research on Online Fault Warning Method for EMU MOA

更新时间：2025-07-23

- Research on Online Fault Warning Method for EMU MOA
- Moren Journal (2025)
- 作者机构：
  
  中车时代电气股份有限公司,湖南省株洲市,中国
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China
- DOI：
  CLC：
- Received：31 October 2024，
  
  Revised：17 December 2024，
  
  Accepted：31 December 2024，
- 稿件说明：
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ZHU Wenlong, LI Qiang, XU Hailong. Research on Online Fault Warning Method for EMU MOA[J/OL]. Moren journal, 2025.
DOI：

ZHU Wenlong, LI Qiang, XU Hailong. Research on Online Fault Warning Method for EMU MOA[J/OL]. Moren journal, 2025. DOI：

摘要

动车组避雷器能够有效预防车内高压设备免受雷电大电流冲击，从而保护动车组安全稳定运行，然而避雷器的故障排查方法多依赖于传统的人工检修，不仅人工成本高，而且检修不及时，极易导致避雷器“带病”运行，造成动车组高压设备稳定运行存在安全隐患。针对避雷器内部受潮、阀片老化、表面污秽积累三种故障类型，本文使用阈值比较法判定避雷器的运行状态，同时针对阈值比较法在判定内部受潮和阀片老化两种主要的故障类型时，易受到网压不稳定和环境温度湿度影响从而导致误报率增加的情况，本文提出了加权阈值比较法和趋势分析法用于实现避雷器的在线故障预警，并通过故障模式试验验证了所提算法的有效性，试验结果符合相应的实际需求。

Abstract

The MOA of the high-speed train can effectively prevent the high-voltage equipment inside the train from being impacted by lightning currents

thereby protecting the safe and stable operation of the high-speed train. However

the fault diagnosis method of the MOA mostly relies on traditional manual maintenance

which not only has high labor costs

but also is not timely maintained

which can easily lead to the "faulty" operation of the MOA and pose safety hazards to the stable operation of the high-voltage equipment of the high-speed train. This paper uses the threshold comparison method to determine the operating status of MOA for four types of faults: disconnection of grounding wire

internal moisture

aging of valve plates

and accumulation of surface pollution. At the same time

considering that the threshold comparison method is easily affected by unstable network voltage and environmental temperature and humidity when determining the two main types of faults: internal moisture and aging of valve plates

leading to an increase in false alarm rate

this paper proposes the weighted threshold comparison method and trend analysis method to achieve online fault warning of MOA. The effectiveness of the proposed algorithm is verified through fault mode experiments

and the experimental results meet the corresponding practical needs.

关键词

Keywords

references

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