Abstract: Clustering ensemble is the combination of some independent cluster’s results,so as to get an optimal clustering result to the original data.Clustering ensemble can reduce the influence of noise and outlier on the clustering result,and at the same time it can also improve the robustness and stability of the clustering results.This paper divided three aspects to describe the fault diagnosis of high-speed rail analysis based on clustering ensemble.In the first aspect,we switched the original simulation data from time domain to frequency domain through discrete Fourier transform,and used different feature selection algorithms for data preprocessing.In the second aspect,we used AP,FCM,EmGauussian and Kmeans,four different clustering algorithms,to analyze it.In the last aspect,we used HGPA,MCLA and CSPA,three different Cluster Ensemble models,to integration the results of clustering algorithms.This paper applied clustering ensemble algorithm in fault diagnosis of high-speed rail for the first time.The experimental results show that this method has better performance than a single clustering algorithm,and can be more accurate and effective for fault diagnosis of high-speed rail.

Key words: Fault diagnosis,Feature selection,Cluster analysis,Clustering ensemble