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

doi:10.11772/j.issn.1001-9081.2015.10.2819

Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (10): 2819-2823.DOI: 10.11772/j.issn.1001-9081.2015.10.2819

Previous Articles Next Articles

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

CHEN Zhi, LI Tianrui, LI Ming, YANG Yan

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

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

西南交通大学信息科学与技术学院, 成都 611756

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)。

Abstract

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

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

CLC Number:

TP301.6

CHEN Zhi, LI Tianrui, LI Ming, YANG Yan. Fault diagnosis method of high-speed rail based on compute unified device architecture[J]. Journal of Computer Applications, 2015, 35(10): 2819-2823.

陈志, 李天瑞, 李明, 杨燕. 基于计算统一设备架构的高铁故障诊断方法[J]. 计算机应用, 2015, 35(10): 2819-2823.

References

[1] WANG S. Development and economic impact analysis of Chinese high-speed railway[J]. Journal of Southwest Jiaotong University, 2010, 11(5): 65-69.(王顺洪. 中国高速铁路发展及其经济影响分析[J]. 西南交通大学学报, 2010, 11(5): 65-69.)
[2] ZHAO C, LI T, WANG Z, et al. High-speed rail vibration data preprocessing based on MapReduce[J]. Journal of Nanjing University: Natural Science, 2012, 48(4): 390-396.(赵成兵, 李天瑞, 王仲刚,等. 基于MapReduce的高铁振动数据预处理[J]. 南京大学学报:自然科学版, 2012, 48(4): 390-396.)
[3] QIN N, JIN W, HUANG J, et al. Feature extraction of high speed train bogie based on ensemble empirical mode decomposition and sample entropy[J]. Journal of Southwest Jiaotong University, 2014, 49(1): 27-32.(秦娜, 金炜东, 黄进, 等. 基于EEMD样本熵的高速列车转向架故障特征提取[J]. 西南交通大学学报, 2014,49(1): 27-32.)
[4] ZHAO J, YANG Y, LI T, et al. Fault diagnosis of high-speed rail based on approximate entropy and empirical mode decomposition[J]. Computer Science, 2014, 41(1): 91-94,99.(赵晶晶,杨燕,李天瑞,等. 基于近似熵及EMD的高铁故障诊断[J]. 计算机科学, 2014,41(1): 91-94,99.)
[5] QIN N, JIN W, HUANG J, et al. Fault diagnosis of high speed train bogie based on ensemble empirical mode decomposition[J]. Computer Engineering, 2013, 39(12): 1-4.(秦娜, 金炜东, 黄进,等. 基于EEMD的高速列车转向架故障诊断[J]. 计算机工程, 2013, 39(12): 1-4.)
[6] QIN N, WANG K, JIN W, et al. Fault feature analysis of high-speed train bogie based on empirical mode decomposition entropy[J]. Journal of Traffic and Transportation Engineering, 2014, 14(1): 57-64,74.(秦娜, 王开云, 金炜东,等. 高速列车转向架故障的经验模态熵特征分析[J]. 交通运输工程学报, 2014, 14(1): 57-64,74.)
[7] OWENS J D, HOUSTON M, LUEBKE D, et al. GPU computing[J]. Proceedings of the IEEE, 2008, 96(5): 879-899.
[8] NICKOLLS J, BUCK I, GARLAND M, et al. Scalable parallel programming with CUDA[J]. Queue, 2008, 6(2): 40-53.
[9] CHENG F, LI D. CUDA-based parallel implementation of the Adaboost algorithm [J]. Computer Engineering and Science, 2011, 33(2):118-123.(程峰, 李德华. 基于CUDA的Adaboost算法并行实现[J]. 计算机工程与科学, 2011, 33(2): 118-123.)
[10] OGAWA K, ITO Y, NAKANO K. Efficient Canny edge detection using a GPU[C]//Proceedings of the First IEEE International Conference on Networking and Computing. Piscataway: IEEE Press, 2010: 279-280.
[11] IWAI K, NISHIKAWA N, KUROKAWA T. Acceleration of AES encryption on CUDA GPU[J]. International Journal of Networking and Computing, 2012, 2(1): 131-145.
[12] XU X, XU G, WANG X, et al. Empirical mode decomposition and its application[J]. Acta Electronica Sinica, 2009, 37(3): 581-585.(徐晓刚, 徐冠雷, 王孝通,等. 经验模式分解(EMD)及其应用[J]. 电子学报, 2009, 37(3): 581-585.)
[13] SHEN Z, CHEN X, ZHANG X, et al. A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM[J]. Measurement, 2012, 45(1): 30-40.
[14] XU Y, LI L, HE Z. Approximate entropy and its applications in mechanical fault diagnosis[J]. Information and Control, 2002, 31(6): 547-551.(胥永刚, 李凌均, 何正嘉. 近似熵及其在机械设备故障诊断中的应用[J]. 信息与控制, 2002, 31(6): 547-551.)
[15] de LUCA A, TERMINI S. A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory[J]. Information and Control, 1972, 20(4): 301-312.
[16] ZHANG M, ZHOU Z. ML-KNN: a lazy learning approach to multi-label learning[J]. Pattern Recognition, 2007, 40(7): 2038-2048.
[17] DONG L, GE W, CHEN K. Study on application of parallel computation on CUDA[J]. Information Technology, 2010(4): 11-15. (董荦, 葛万成, 陈康力. CUDA并行计算的应用研究[J]. 信息技术, 2010(4): 11-15.)

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

基于计算统一设备架构的高铁故障诊断方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	XIE Wenbo, WEI Yongzhuang, LIU Zhenghong. Parallel implementation and analysis of SKINNY encryption algorithm using CUDA [J]. Journal of Computer Applications, 2021, 41(4): 1136-1141.
[2]	YANG Xianfeng, GUI Hongjun, FU Chunchang. F-X domain predictive filtering parallel algorithm based on compute unified device architecture [J]. Journal of Computer Applications, 2021, 41(2): 486-491.
[3]	MENG Xinyu, WANG Ruihan, ZHANG Xiping, WANG Mingjie, QIU Gang, WANG Zhengxia. Ultra-short-term wind power prediction based on empirical mode decomposition and multi-branch neural network [J]. Journal of Computer Applications, 2021, 41(1): 237-242.
[4]	ZHANG Xiaohan, FENG Aimin. Short-term traffic flow prediction based on empirical mode decomposition and long short-term memory neural network [J]. Journal of Computer Applications, 2021, 41(1): 225-230.
[5]	SUN Shuguang, ZHOU Qi. Remote real-time fault diagnosis for automatic flight control system based on residual decision [J]. Journal of Computer Applications, 2020, 40(5): 1522-1528.
[6]	WANG Rijun, BAI Yue, ZENG Zhiqiang, DUAN Nengquan, DANG Changying, DU Wenhua, WANG Junyuan. Multi-sensor fault diagnosis method for quad-rotor aircraft based on adaptive observer [J]. Journal of Computer Applications, 2018, 38(9): 2735-2741.
[7]	MA Mengying, ZENG Yali, WEI Tiantian, CHEN Zhide. Fault diagnosis algorithm of WSN based on precondition of neighbor nodes [J]. Journal of Computer Applications, 2018, 38(8): 2348-2352.
[8]	WANG Yan, LUO Qian, DENG Hui. Bearing fault diagnosis method based on Gibbs sampling [J]. Journal of Computer Applications, 2018, 38(7): 2136-2140.
[9]	MO Zan, ZHAO Bing, HUANG Yanying. Network public opinion prediction by empirical mode decomposition-autoregression based on extreme gradient boosting model [J]. Journal of Computer Applications, 2018, 38(3): 615-619.
[10]	HE Chun, LI Qi, WU Ranghao, LIU Bangxin. Diagnosis of fault circuit by modularized BP neural network based on fault propagation [J]. Journal of Computer Applications, 2018, 38(2): 602-609.
[11]	XIONG Ningxin, WANG Yingming. Multiple attribute decision method based on improved fuzzy entropy and evidential reasoning [J]. Journal of Computer Applications, 2018, 38(10): 2801-2806.
[12]	XU Bingke, ZHOU Yuzhe, YANG Maolin, XIE Yuanhang, LI Xiaoyu, LEI Hang. Alarm-filtering algorithm of alarm management system for telecom networks [J]. Journal of Computer Applications, 2018, 38(10): 2881-2885.
[13]	YANG Hongbai, ZHANG Hongli, LIU Shulin. Rolling bearing fault diagnosis based on visual heterogeneous feature fusion [J]. Journal of Computer Applications, 2017, 37(4): 1207-1211.
[14]	ZHAO Yongtao, CHEN Qingkui, FANG Yuling, ZHAO Deyu, JI Lina. Early warning method for driving safety based on CUDA [J]. Journal of Computer Applications, 2017, 37(1): 134-137.
[15]	JI Lina, CHEN Qingkui, CHEN Yuanjing, ZHAO Deyu, FANG Yuling, ZHAO Yongtao. Real-time crowd counting method from video stream based on GPU [J]. Journal of Computer Applications, 2017, 37(1): 145-152.