Study on intelligent diagnosis and early warning system for traction drive and auxiliary power systems of urban rail transit trains

CAO Xuejie; HE Ye; SHAN Sheng; ZHOU Moling; ZHAN Yanhao; CHEN Lingquan

doi:10.13890/j.issn.1000-128X.2022.06.012

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Study on intelligent diagnosis and early warning system for traction drive and auxiliary power systems of urban rail transit trains

Electric Traction and Control | 更新时间：2024-08-02

- Study on intelligent diagnosis and early warning system for traction drive and auxiliary power systems of urban rail transit trains
- Electric drive for locomotives Issue 6, Pages: 80-86(2022)
- 作者机构：
  
  1.株洲中车时代电气股份有限公司数据与智能技术中心，湖南株洲 412001
  2.广州地铁集团有限公司，广东广州;510000
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2022.06.012
  CLC： U239.5;U269.32⁺2
- Published：10 November 2022，
  
  Received：27 September 2021，
- 稿件说明：
扫描看全文
CAO Xuejie, HE Ye, SHAN Sheng, et al. Study on intelligent diagnosis and early warning system for traction drive and auxiliary power systems of urban rail transit trains. [J]. Electric drive for locomotives (6):80-86(2022)
DOI：

CAO Xuejie, HE Ye, SHAN Sheng, et al. Study on intelligent diagnosis and early warning system for traction drive and auxiliary power systems of urban rail transit trains. [J]. Electric drive for locomotives (6):80-86(2022) DOI： 10.13890/j.issn.1000-128X.2022.06.012.

摘要

牵引传动系统和辅助供电系统是城轨列车的核心子系统，为提高牵引及辅助供电系统运行可靠性，通过对现有系统检修维护方式展开数据分析、现场调研，总结牵引及辅助系统故障诊断预警需求，基于外部故障现象、运行环境数据、故障数据等信息建立了一套城轨列车牵引及辅助系统在线故障诊断预警系统。考虑牵引及辅助系统故障特征信息复杂且具有模糊性，研究利用长短时记忆神经网络模型实现故障预警，利用模糊专家诊断方法实现故障诊断。该系统由车载网络系统、车载数据处理中心、地面大数据平台组成，自动完成故障数据生成、推送预警、故障诊断结果输出和检修排查建议，提高整车的可用性以及检修维护效率。现场试验结果表明，该系统可提前预警城轨列车牵引辅助系统大部分故障项点的异常及定位故障，优化了检修维护流程并提高检修维护效率。

Abstract

The traction drive system and auxiliary power system are two core subsystems of urban rail transit trains. In order to improve their operational reliability

this paper proposed an online fault diagnosis and early warning system based on massive data information regarding external fault symptoms

operational environment

faults and so forth

carried out the data analysis and field investigation on the existing maintenance mode of these two subsystems

and summarization on their requirements of fault diagnosis and early warning. Considering that their fault characteristics information is complicated and fuzzy

the long short-term memory (LSTM) neural network model was built to realize fault early warning

and the fuzzy expert diagnosis method was used to realize fault diagnosis in the current study. Consisting of an onboard network system

onboard data processing center and ground big data platform

this online system can automatically generate fault data

push early warnings

fault diagnosis results output and troubleshooting suggestions

obviously improving train availability and maintenance efficiency. According to the field test results

this system can push early warnings in case of any abnormality of most fault items and locate faults of the two subsystems

thus optimizing the maintenance procedure and improving the maintenance efficiency.

关键词

城轨列车牵引传动系统辅助供电系统智能运维故障诊断模糊专家诊断LSTM

Keywords

urban rail transit traintraction drive systemauxiliary power systemintelligent operation and maintenancefault diagnosisfuzzy expert diagnosisLSTM

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