摘要
多功能车辆总线(MVB)用于列车通信网络中各功能设备间的信息传输,其网络异常将严重影响列车运行安全。在对MVB网络常见故障分析的基础上,提出一种基于变分自编码器(VAE)的MVB网络异常检测方法,直接将采集到的MVB信号物理波形作为模型输入,选取VAE重构误差作为MVB网络异常检测依据。为了有效解决实际应用中带标记异常数据不足的问题,VAE采用半监督学习方式,在训练阶段只需要正常数据。根据MVB网络正常数据的重构误差,设计MVB网络节点健康指标,并采用核密度估计方法自动确定异常检测阈值,而不依赖于专家经验。实验结果表明,该方法能够有效处理高维度数据和学习MVB信号物理波形内在特征,相比于传统方法具有更好的网络异常检测表现。
Multifunction Vehicle Bus(MVB)is used to transfer information among devices in the train communication network,whose anomaly will endanger the safety of train operation.Based on the analysis of MVB common faults,an anomaly detection method was proposed for the MVB network based on variational autoencoder(VAE),where the physical layer waveforms of MVB signals collected were directly used as input of VAE model and the reconstruction error of the VAE model was defined as the basis of the MVB network anomaly detection.In the training phase,the VAE model was trained by only using the normal data in the semi-supervised learning manner,which can solve the problem of the lack of labeled anomaly samples in practice.The health indicator of the MVB network node was designed according to the reconstruction error of the normal data of the MVB network,and the kernel density estimation method was applied to determine the decision threshold in this case only normal samples were provided without relying on expert experience.The experimental results show that the proposed method,capable of handling the high-dimensional samples and learning the internal features of the MVB waveforms effectively,has higher performance than the traditional methods in anomaly detection.
作者
杨岳毅
王立德
陈煌
王冲
YANG Yueyi;WANG Lide;CHEN Huang;WANG Chong(School of Electrical Engineering,Beijing Jiaotong University,Beijing 100044,China)
出处
《铁道学报》
EI
CAS
CSCD
北大核心
2022年第1期71-78,共8页
Journal of the China Railway Society
基金
中国国家铁路集团有限公司科技研究开发计划(N2020J007)。
关键词
MVB网络
异常检测
变分自编码器
核密度估计
MVB network
anomaly detection
variational autoencoder
kernel density estimation
