基于OC-SVM与DNN相结合的ZPW-2000R轨道电路故障诊断研究

Fault Diagnosis of ZPW-2000R Track Circuits Based on OC-SVM and DNN

针对轨道电路故障诊断准确率低且高质量故障数据难以收集等问题,提出一种基于单分类支持向量机(OC-SVM)与深度神经网络(DNN)相结合的故障诊断方法.该方法使用OC-SVM模型对数据进行单分类识别,将正样本数据输入到DNN模型进行训练和预测,为负样本数据添加标签并收集.利用ZPW-2000R轨道电路信号数据进行大量实验,结果表明OC-SVM模型能精确地识别出正负样本数据,DNN模型能准确高效地诊断出15种数据类型,且准确率高达99%.与粒子群算法优化支持向量机、卷积神经网络、堆叠自编码器3种故障诊断方法相比,该组合方法的准确率更高,诊断效果更稳定.

To address the problems of low accuracy for fault diagnosis for track circuits and difficulty in collecting high-quality fault data,a fault diagnosis method based on the combination of one-class support vector machine(OC-SVM)and deep neural network(DNN)is proposed.This method uses OC-SVM model to perform single-class recognition of data.The positive sample data are input into DNN model for training and prediction to label and collect the negative sample data.A large number of experiments are conducted using ZPW-2000R track circuit signal data.Results show that the OC-SVM model can accurately identify the positive and negative sample data,and DNN model can accurately and efficiently diagnose 15 types of data with an accuracy rate of 99%.Compared with the three fault diagnosis methods of particle swarm algorithm optimized support vector machine,convolutional neural network and stacked self-encoder,this combined method has a higher accuracy and more stable diagnosis effectiveness.