基于聚类集成的高铁故障诊断分析  

Fault Diagnosis of High-speed Rail Based on Clustering Ensemble

作　　者：陈云风[1] 王红军[1] 杨燕[1] 

机构地区：[1]西南交通大学信息科学与技术学院,成都610031

出　　处：《计算机科学》2015年第6期233-238,共6页Computer Science

基　　金：国家自然科学基金项目:藏文Web信息的社会网络动态演化机理研究(61262058);西南交通大学牵引动力国家重点实验室自主研究课题:基于云计算的海量高铁数据处理关键技术研究(2012TPL_T15)资助

摘　　要：聚类集成是对若干独立基聚类器的结果进行组合,从而得到一个对原始数据最优的聚类结果。聚类集成能够减小噪声和孤立点对结果的影响,同时增强聚类结果的鲁棒性和稳定性。从3方面阐述了基于聚类集成的高铁故障诊断分析:1)将原始高铁仿真数据通过傅里叶变化把信号从时域转换到频域,再用不同的特征选择算法进行数据预处理分析;2)分别采用Affinity Propagation(AP)、模糊C均值(FCM)、高斯混合模型(EmGauussian)、Kmeans 4种不同的聚类算法对预处理后的数据进行分析比较;3)引入HGPA、MCLA、CSPA 3种不同聚类集成模型,将得到的基聚类结果分别进行集成。首次把聚类集成算法运用于高铁故障分析中,对比实验结果表明,该方法相比于单个的聚类算法能够更准确有效地进行高铁故障诊断。Clustering ensemble is the combination of some independent cluster＇s results,so as to get an optimal cluste- ring result to the original data. Clustering ensemble can reduce the influence of noise and outlier on the clustering re- suit, 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 cluste- ring 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.

关 键 词：故障诊断 特征选择 聚类分析 聚类集成 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]