JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (1): 7-14.doi: 10.6040/j.issn.1672-3961.1.2016.330

Previous Articles     Next Articles

Condition recognition of high-speed train based on multi-view classification ensemble

GUO Chao1, YANG Yan1*, JIANG Yongquan2, SONG Yi1   

  1. 1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, Sichuan, China;
    2. State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, Sichuan, China
  • Received:2016-03-31 Online:2017-02-20 Published:2016-03-31

PDF (PC)

853

Abstract: To solve the problem about the incompletion of feature extraction and inaccuracy of the identification performance of traditional method, a multi-view classification ensemble method(MVCE)for condition recognition of high speed train was proposed. The method combined with multi-view feature extraction and classification ensemble technology. For condition recognition, the wavelet energy, spectral coefficients and ensemble empirical mode decomposition fuzzy entropy were extracted from three angles: the characteristics of the signal, the frequency domain and the time-frequency domain. The Fisher ratio was used to perform feature selection for the frequency domain features of the high speed train vibration signal, then the feature of the three views were constructed collectively. The least square support vector machine(LSSVM)and the K nearest neighbor(KNN)classifiers were used to recognize each view. The output results of multiple classifiers were integrated by using the classification entropy voting principle. The experimental results showed that the average recognition rate of the proposed method on the simulation data and the laboratory data were 89.18% and 90.87% respectively. Meanwhile, the comparative results illustrated the completeness of the features extracted by the method and the validity of the ensemble model with diversity.

Key words: multi-view, classification ensemble, high-speed train, condition recognition, feature extraction

CLC Number: 

  • TP391
[1] ZHAO Jingjing, YANG Yan, LI Tianrui, et al. Application of empirical mode decomposition and fuzzy entropy to HST fault diagnosis[C] // Proceedings of the 8th International Conference on Intelligent Systems and Knowledge Engineering. Shenzhen: Springer-Verlag, 2014:93-103.
[2] 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. ZHAO Jingjing, YANG Yan, LI Tianrui, et al. Fault diagnosis of high-speed rail based on approximate entropy and empirical mode decomposition[J]. Computer Science, 2014, 41(1):91-94.
[3] XIAO Wenchao, YANG Yan, WANG Hongjun, et al. Semi-supervised hierarchical clustering ensemble and its application[J]. Neurocomputing, 2016, 173:1362-1376.
[4] 丁康, 黄志东, 林慧斌. 一种谱峭度和Morlet小波的滚动轴承微弱故障诊断方法[J]. 振动工程学报, 2014, 27(1):128-135. DING Kang, HUANG Zhidong, LIN Huibin. A weak fault diagnosis method for rolling element bearings based on Morlet wavelet and spectral kurtosis[J]. Journal of Vibration Engineering, 2014, 27(1):128-135.
[5] 张学武, 吕艳云, 丁燕琼,等. 小波统计法的表面缺陷检测方法[J]. 控制理论与应用, 2010, 27(10): 1331-1336. ZHANG Xuewu, LYU Yanyun, DING Yanqiong, et al.Surface defect inspection based on wavelet statistical analysis[J]. Control Theory & Applications, 2010, 27(10):1331-1336.
[6] HU Weihong, LIU Jiyuan, CUI Jianguo, et al. Fault diagnosis of gas turbine based on support vector machine[C] // Proceedings of the 26th Chinese Control and Decision Conference.Changsha: IEEE, 2014:2853-2856.
[7] 秦娜, 金炜东, 黄进,等. 高速列车转向架故障信号的聚合经验模态分解和模糊熵特征分析[J]. 控制理论与应用, 2014, 31(9):1245-1251. QIN Na, JIN Weidong, HUANG Jin, et al.Ensemble empirical mode decomposition and fuzzy entropy infault feature analysis for high-speed train bogie[J]. Control Theory & Applications, 2014, 31(9):1245-1251.
[8] 王磊, 纪国宜. 基于EMD模糊熵和SVM的转子系统故障诊断[J]. 噪声与振动控制, 2012, 32(3):171-176. WANG Lei, JI Guoyi. Fault diagnosis of rotor system based on EMD fuzzy entropy and SVM[J]. Noise and Vibration Control, 2012, 32(3):171-176.
[9] 陈云风, 王红军, 杨燕. 基于聚类集成的高铁故障诊断分析[J]. 计算机科学, 2015, 42(6):233-238. CHEN Yunfeng, WANG Hongjun, YANG Yan. Fault diagnosis of high-speed rail based on clustering ensemble[J]. Computer Science, 2015, 42(6):233-238.
[10] WANG Jiao, LUO Siwei, LI Yan. A multi-view regularization method for semi-supervised learning[C] // Proceedings of 7th International Symposium on Neural Networks. Shanghai:Springer-Verlag, 2010:444-449.
[11] 谭爱平, 陈浩, 吴伯桥. 基于SVM的网络入侵检测集成学习算法[J]. 计算机科学, 2014, 41(2):197-200. TAN Aiping, CHEN Hao, WU Boqiao.Network intrusion intelligent detection algorithm based on adaboost[J]. Computer Science, 2014, 41(2):197-200.
[12] REMYA K, RAMYA J. Using weighted majority voting classifier combination for relation classification in biomedical texts[C] // Proceedings of theInternational Conference on Control, Instrumentation, Communication and Computational Technologies.Guilin: IEEE, 2014:1205-1209.
[13] 李鑫滨, 陈云强, 张淑清. 基于LS-SVM多分类器融合决策的混合故障诊断算法[J]. 振动与冲击, 2013, 32(19):159-164. LI Xinbin, CHEN Yunqiang, ZHANG Shuqing. Hybrid fault diagnosis algorithm based on fusion decision of multiple LS-SVM classifiers[J]. Journal of Vibration and Shock, 2013, 32(19):159-164.
[14] ZABIDI A, MANSOR W, KHUAN L, et al. The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron neural network[C] //Proceedings of IEEE EMBS Conference on Biomedical Engineering and Sciences.Kuala Lumpur: IEEE, 2010:126-129.
[15] KROGH A, VEDELSBY J. Neural network ensembles, cross validation, and active learning[J]. Advances in Neural Information Processing Systems, 1995, 7(10):231-238.
[16] YAN Xin, CHOWDHURY N. Mid-term electricity market clearing price forecasting: a hybrid LSSVM and ARMAX approach[J]. International Journal of Electrical Power & Energy Systems, 2013, 53(4):20-26.
[17] XIE Baile, MINN H. Real-time sleep apnea detection by classifier combination[J]. IEEE Transactions on Information Technology in Biomedicine, 2012, 16(3):469-477.
[18] XIE Jipeng, YANG Yan, WANG Hao, et al. Fault diagnosis in high-speed train running gears with improved deep belief networks[J].Journal of Computational Information Systems, 2015, 11(24):7723-7730.
[19] POLIKAR R. Ensemble based systems in decision making[J]. IEEE Circuits & Systems Magazine, 2006, 6(3):21-45.
[20] ZHANG Junming, WU Yan, BAI Jing, et al. Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers[J]. Transactions of the Institute of Measurement & Control, 2016, 38(4):435-451.
[1] WANG Guoxin, CHEN Fengdong, LIU Guodong. Feature extraction method of color pseudo-random coded structured light [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(5): 55-60.
[2] ZHANG Peirui, YANG Yan, XING Huanlai, YU Xiuying. Incremental multi-view clustering algorithm based on kernel K-means [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 48-53.
[3] YE Ziyun, YANG Jinfeng. A finger-vein recognition method based on weighted graph model [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 103-109.
[4] ZHANG Zhenyue, LI Fei, JIANG Mingyan. Unsupervised face image feature extraction based on low-rank representation projection [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(1): 15-20.
[5] YU Qingmin, LI Xiaolei, ZHAI Yong. Feature extraction method of rolling bearing inner ring in wind turbine based on improved EMD and feature box [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(3): 89-95.
[6] ZHANG Yuling, YIN Chuanhuan. Android malware detection based on SVM [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(1): 42-47.
[7] ZHONG Zhiyan, WEN Zhiqiang, ZHANG Xiaoyun, YE Degang. Neighborhood similarity descriptor used in halftone image [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(3): 58-64.
[8] NIU Jiqiang, LIANG Xifeng, XIONG Xiaohui, LIU Feng. Effect of outside vehicle windshield on aerodynamic performance of high-speed train under crosswind [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(2): 108-115.
[9] LU Dan, ZHOU Yiqi. Vibration analysis of excavator seat based on EEMD and CWT [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(3): 58-64.
[10] ZHOU Zhe, SHANG Lin. A sentiment analysis method based on dynamic lexicon and three-way decision [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(1): 19-23.
[11] YU Hai-jing1,2, LI Gui-ju1*. Color smoke image recognition based on differential box-counting fractal dimension algorithm [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(1): 35-40.
[12] GAO Shuang1,2, ZHANG Hua-xiang1,2*, FANG Xiao-nan1,2. Independent component analysis and co-training based Web spam detection [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2013, 43(2): 29-34.
[13] LI Hui1,2, HU Yun1,3, LI Cun-hua1. The technique of gas disaster information feature extraction based on rough set theory [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2012, 42(5): 91-95.
[14] CAO Hong-gen1, YUAN Bao-hua1, ZHU Hui-sheng2. Recognition of intersected face based on contrast information and  local binary pattern [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2012, 42(4): 29-34.
[15] ZHANG Si-yi1,2, WANG Shi-tong1*. Kernelized spatial depth function for the feature extraction method [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2012, 42(3): 45-51.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd