Fault detection of JTC trackside equipment based on tSNE multi-feature fusion
The reliability of the trackside equipment of the jointless track circuit(JTC)will gradually decrease during long-term outdoor operations,which poses a severe threat to the safety of train operations.Aiming at the problems of complex fault types and insufficient fault feature extraction of trackside equipment in JTC fault diagnosis research,a fault detection model of JTC trackside equipment based on t-distribution stochastic neighbor embedding(tSNE)multi-feature fusion was proposed.Firstly,according to the influence of different trackside equipment faults on track circuit reader(TCR)induced voltage signals,the fault characteristics of each trackside equipment were analyzed.Secondly,the amplitude domain features,such as the variance,root-mean-square,and peak factor of the TCR induced voltage signal,were extracted to form the original fault feature set with the permutation entropy and dispersion entropy.To remove the redundant information and obtain the fusion manifold features with high discrimination,the tSNE algorithm was used for feature fusion.Finally,the fault detection confusion matrix was obtained through the deep residual network(DRN)to realize the fault location of the trackside equipment.The experimental results show that the features fused by tSNE have larger inter-class distances between heterogeneous fault samples and smaller intra-class distances between homogeneous fault samples.Compared with principal component analysis(PCA),stochastic proximity embedding(SPE),and stochastic neighbor embedding(SNE)algorithms,tSNE has a better feature extraction effect.In addition,combined with DRN,it can effectively identify various trackside equipment faults,and the fault detection accuracy can reach 98.28%.The example verification results of field signals show that the proposed fault detection model can meet the actual needs of the railway field for fault location of outdoor equipment.
万方数据
维普
