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。
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.
出处
《计算机应用》
CSCD
北大核心
2015年第10期2819-2823,共5页
journal of Computer Applications
基金
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(2012TPL T15)
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
fault diagnosis
Compute Unified Device Architecture （CUDA）
Empirical Mode Decomposition （EMD）
fuzzy entropy
K-Nearest Neighbor （KNN） classification algorithm
