计算机应用 ›› 2015, Vol. 35 ›› Issue (10): 2819-2823.DOI: 10.11772/j.issn.1001-9081.2015.10.2819

• 第十五届中国机器学习会议(CCML2015)论文 • 上一篇    下一篇

基于计算统一设备架构的高铁故障诊断方法

陈志, 李天瑞, 李明, 杨燕   

  1. 西南交通大学 信息科学与技术学院, 成都 611756
  • 收稿日期:2015-06-01 修回日期:2015-07-06 出版日期:2015-10-10 发布日期:2015-10-14
  • 通讯作者: 陈志(1990-),男,四川成都人,硕士研究生,CCF会员,主要研究方向:云计算、机器学习,905833099@qq.com
  • 作者简介:李天瑞(1969-),男,福建莆田人,教授,博士,CCF高级会员,主要研究方向:智能信息处理、数据挖掘、云计算;李明(1990-),男,四川资阳人,硕士研究生,CCF会员,主要研究方向:云计算、机器学习;杨燕(1964-),女,安徽合肥人,教授,博士,博士生导师,CCF高级会员,主要研究方向:数据挖掘、计算智能、集成学习。
  • 基金资助:
    国家自然科学基金资助项目(61175047);国家重点实验室自主研究课题资助项目(2012TPLT15)。

Fault diagnosis method of high-speed rail based on compute unified device architecture

CHEN Zhi, LI Tianrui, LI Ming, YANG Yan   

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu Sichuan 611756, China
  • Received:2015-06-01 Revised:2015-07-06 Online:2015-10-10 Published:2015-10-14

摘要: 为解决传统高铁振动信号故障诊断方法速度慢、难以满足实时处理的要求,提出一种基于计算统一设备架构(CUDA)加速的高铁振动信号故障诊断方法。首先利用CUDA架构对高铁数据进行经验模态分解(EMD),进而计算分解所得到的各个分量的模糊熵,最后利用最近邻分类(KNN)算法对多个模糊熵特征组成的特征空间进行故障分类。实验结果表明,该方法能高效地对高铁振动信号进行故障分类,运行速度较传统方法有明显提高。

关键词: 故障诊断, 计算统一设备架构, 经验模态分解, 模糊熵, 最近邻分类算法

Abstract: Concerning the problem that traditional fault diagnosis of High-Speed Rail (HSR) vibration signal is slow and cannot meet the actual requirement of real-time processing, an accelerated fault diagnosis method for HSR vibration signal was proposed based on Compute Unified Device Architecture (CUDA). First, the data of HSR was processed by Empirical Mode Decomposition (EMD) based on CUDA, then the fuzzy entropy of each result component was calculated. Finally, K-Nearest Neighbor (KNN) classification algorithm was used to classify feature space which consisted of multiple fuzzy entropy features. The experimental results show that the proposed method is efficient on fault classification of HSR vibration signal, and the processing speed is significantly improved compared with the traditional method.

Key words: fault diagnosis, Compute Unified Device Architecture (CUDA), Empirical Mode Decomposition (EMD), fuzzy entropy, K-Nearest Neighbor (KNN) classification algorithm

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