基于多视图分类集成的高铁工况识别

doi:10.6040/j.issn.1672-3961.1.2016.330

山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (1): 7-14.doi: 10.6040/j.issn.1672-3961.1.2016.330

基于多视图分类集成的高铁工况识别

郭超¹,杨燕^1*,江永全²,宋祎¹

1. 西南交通大学信息科学与技术学院, 四川成都 611756;2. 西南交通大学牵引动力国家重点实验室, 四川成都 610031

收稿日期:2016-03-31 出版日期:2017-02-20 发布日期:2016-03-31
通讯作者: 杨燕(1964— ),女,安徽合肥人,教授,博导,博士,主要研究方向为数据挖掘与集成学习.E-mail:yyang@swjtu.edu.cn E-mail:guochao@my.swjtu.edu.cn
作者简介:郭超(1992— ),男,河南濮阳人,硕士研究生,主要研究方向为智能信息处理.E-mail:guochao@my.swjtu.edu.cn
基金资助:
国家自然科学基金资助项目(61134002,61572407);国家科技支撑计划资助项目(2015BAH19F02);四川省科技支撑计划资助项目(2014SZ0207)

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

GUO Chao¹, YANG Yan^1*, JIANG Yongquan², SONG Yi¹

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

摘要/Abstract

摘要： 针对传统方法识别高铁工况存在特征提取不完备和识别性能不精确的问题,提出一种多视图分类集成的高铁工况识别方法(MVCE)。该方法结合多视图特征提取和分类集成技术,从信号本身特性、频域和时频域三个角度提取小波能量、频谱系数、聚合经验模态分解模糊熵,并使用Fisher比率对其频域特征进行特征选择,从而构建高铁振动信号三个视图的特征。使用最小二乘支持向量机和K最近邻分类器分别对每个视图的特征进行初步识别。最后采用分类熵投票策略对多个分类器输出结果进行集成。试验结果表明:该方法对仿真数据和实验室数据的平均识别率分别达到89.18%和90.87%。同时对比结果说明了该方法提取特征的完备性和具有多样性集成模型的有效性。

关键词: 工况识别, 特征提取, 多视图, 分类集成, 高速列车

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

中图分类号:

TP391

郭超,杨燕,江永全,宋祎. 基于多视图分类集成的高铁工况识别[J]. 山东大学学报(工学版), 2017, 47(1): 7-14.

GUO Chao, YANG Yan, JIANG Yongquan, SONG Yi. Condition recognition of high-speed train based on multi-view classification ensemble[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(1): 7-14.

参考文献

[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.

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基于多视图分类集成的高铁工况识别

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

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