基于模糊聚类和CNN-BIGRU的轨道电路故障预测

Fault Prediction Method of Track Circuit Based on Fuzzy Clustering and CNN-BIGRU

针对轨道电路稳态环境下故障诊断时效性不足的问题,提出一种基于Gath-Geva(GG)模糊聚类对轨道电路退化状态进行划分,并利用卷积神经网络(convolutional neural network,简称CNN)和双向门控循环单元(bi-directional gated recurrent unit,简称BIGRU)进行轨道电路故障预测的方法。首先,通过集中监测设备获取ZPW-2000轨道电路各类故障发生前一定时间内的正常工作数据;其次,通过核主成分分析进行特征降维和GG模糊聚类对轨道电路性能退化状态进行阶段划分,识别不同的退化状态;最后,利用CNN-BIGRU混合神经网络挖掘轨道电路不同故障类型数据特征,对轨道电路退化状态所对应的故障类型进行预测。实验结果表明,该算法可以精确划分轨道电路退化状态并实现故障预测,CNN-BIGRU预测模型分类精确度可达97.62%,运行时间仅为13.18 s,能够为轨道电路的多模式健康状态识别提供一种有效的方法。

Aiming at the problem of insufficient timeliness of fault diagnosis of track circuit in steady-state envi-ronment,a method based on Gath-Geva(GG)fuzzy clustering to divide the degraded state of track circuit is proposed,and the fault prediction of track circuit is carried out by using convolutional neural network(CNN)and bi-directional gated recurrent unit(BIGRU).Firstly,through the centralized monitoring equipment,the normal working data of each fault type of ZPW-2000 track circuit within a certain time before the fault occurs are obtained.Then,the performance degradation states of track circuit are divided into stages by feature reduc-tion and GG fuzzy clustering based on kernel principal component analysis,and different degradation states are identified.Finally,CNN-BIGRU hybrid neural network is used to mine the data characteristics of different fault types of track circuit,predicting the fault types corresponding to the degraded state of track circuit.Experimen-tal results show that the algorithm can accurately divide the degraded state of track circuit and realize its fault prediction.The classification accuracy of CNN-BIGRU prediction model can reach 97.62%and the running time is only 13.18 s.It can provide an effective method for multi-mode health state recognition of track circuit.