基于TimeGAN的轨道交通LTE-M故障预测研究  

Research on LTE-M Fault Prediction of Rail Transit Based on TimeGAN

作　　者：余凤琴 邹劲柏 沙宏 YU Fengqin;ZOU Jinbai;SHA Hong(School of Railway Transportation,Shanghai Institute of Technology,Shanghai 201418,China;Telecom and Signal Branch,Shanghai Metro Maintenance Support Co.,Ltd.,Shanghai 200235,China)

机构地区：[1]上海应用技术大学轨道交通学院,上海201418 [2]上海地铁维护保障有限公司通号分公司,上海200235

出　　处：《现代信息科技》2025年第8期10-15,共6页Modern Information Technology

基　　金：轨道交通智能运维关键技术研究项目(20090503100);“一带一路”中老铁路工程国际联合实验室(21210750300)。

摘　　要：轨道交通地铁长期演进(LTE-M)网络故障预测数据集存在样本不均衡以及样本数据量小的问题,这些问题影响故障预测的准确性。为了解决上述问题,文章提出了一种基于条件时序生成对抗网络(TimeGAN)的轨道交通LTE-M故障预测研究方法。该方法通过TimeGAN模型中定义动态自编码器和静态自编码器,进一步挖掘轨道交通LTE-M故障数据的动静态特征,并在生成器与判别器的潜在空间引入GELU激活函数,加速模型收敛,生成更接近真实数据的合成数据,从而有效缓解了故障数据集不均衡和数据量小的问题。实验结果表明,使用TimeGAN模型合成的数据进行故障预测训练时,与原始数据相比能产生更好的预测效果。The Long Term Evolution of Metro(LTE-M)network fault prediction dataset of rail transit has the problems of unbalanced samples and small amount of sample data,which impact the accuracy of fault prediction.In order to solve the above problems,this paper proposes a research method of LTE-M fault prediction of rail transit based on conditional Time-series Generative Adversarial Networks(TimeGAN).By defining dynamic autoencoder and static autoencoder in TimeGAN model,this method further explores the dynamic and static characteristics of LTE-M fault data of rail transit,and introduces GELU activation function in the potential space of generator and discriminator to accelerate model convergence and generate synthetic data closer to real data,thus effectively alleviating the problem of unbalanced fault dataset and small data volume.The experimental results show that when the data synthesized by the TimeGAN model is used for fault prediction training,it can produce better prediction results than the original data.

关 键 词：轨道交通LTE-M 故障预测 时间序列 TimeGAN 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]