基于时序知识图谱补全的CTCS-3级列控车载接口设备故障诊断方法  

Fault diagnosis method for train control on-board interface equipment of CTCS-3 based on temporal knowledge graph completion

作　　者：王猛 张大千 周冰艳 马倩影 吕继东 WANG Meng;ZHANG Daqian;ZHOU Bingyan;MA Qianying;LYU Jidong(Beijing HollySys System Engineering Company Limited,Beijing 100176,China;Signal Communication Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;School of Automation and Intelligence,Beijing Jiaotong University,Beijing 100044,China;National Engineering Research Center of Rail Transportation Operation and Control System,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]北京和利时系统工程有限公司,北京100176 [2]中国铁道科学研究院集团有限公司通信信号研究所,北京100081 [3]北京交通大学自动化与智能学院,北京100044 [4]北京交通大学轨道交通运行控制系统国家工程研究中心,北京100044

出　　处：《计算机应用》2025年第2期677-684,共8页journal of Computer Applications

基　　金：国家自然科学基金资助项目(52272329);中国铁路合作发展计划(N2023G058)。

摘　　要：CTCS-3级(Chinese Train Control System-3)列控车载设备在保障列车安全和提高运行效率方面发挥着重要作用。车载接口设备实现车载列车自动防护(ATP)系统与地面设备、司机和列车的交互,然而它的故障在车载设备故障中占比高。为了确定故障原因并保证行车安全,提出一种基于时序知识图谱补全的列控车载接口设备故障诊断方法。首先,采用引入时序的方式整合行车日志和故障统计数据,从而提取故障现象并对齐实体,构建时序知识图谱;其次,构建基于图谱补全的故障诊断网络,融合时序翻译(T-TransE)向量化算法、双向长短期记忆(Bi-LSTM)网络和自注意力(SA)机制提取时序特征;最后,使用某铁路局近几年的车载接口设备故障数据对T-TransE向量化模型进行预训练,选出效果最佳的时序引入方式。为验证所提方法的优越性以及数据结合方式的有效性,使用车载故障数据对不进行数据结合且不进行时序关系引入的故障诊断网络以及其他常见的故障诊断网络进行测试。实验结果表明,在同一语料的情况下,与其他故障诊断框架相比,基于时序知识图谱补全的故障诊断模型正确率最高,达到96.69%。Chinese Train Control System level 3(CTCS-3)train control on-board equipment plays a crucial role in ensuring train safety and improving operational efficiency.On-board interface equipment enables interaction between the onboard Automatic Train Protection(ATP)system,and ground equipment,drivers and trains.However,faults in on-board interface equipment account for a relatively high proportion of on-board equipment faults.In order to identify fault causes and ensure safety,a fault diagnosis method for on-board interface equipment based on temporal knowledge graph completion was proposed.In the method,travel logs and fault statistical data were integrated by introducing the temporal series,which extracted fault phenomena,performed entity alignment,and constructed a temporal knowledge graph.On the basis of the above,a fault diagnosis network based on knowledge graph completion was constructed;Temporal-Translating Embedding(T-TransE)vectorization,and Bidirectional Long Short-Term Memory(Bi-LSTM)network as well as Self-Attention(SA)mechanism were integrated for temporal feature extraction.Finally,the T-TransE vectorization model was pretrained using on-board interface equipment fault data from a railway administration in recent years,and the temporal introduction method with the best effect was selected.In order to validate superiority of the proposed method and effectiveness of the data integration method,the diagnostic network without data integration or temporal relationship introduction,as well as other common fault diagnostic networks,were tested using the on-board fault data.Experimental results show that with the same corpus,the temporal knowledge graph completion-based fault diagnosis model achieves the highest accuracy of 96.69%compared to other fault diagnosis frameworks.

关 键 词：列车运行控制系统 车载接口设备 时间序列 知识图谱 故障诊断 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] TP206[自动化与计算机技术—计算机科学与技术]