Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (5): 1465-1470.doi: 10.13229/j.cnki.jdxbgxb20180559

Previous Articles    

Anomaly detection of rail vehicle gearbox based on multi⁃sensor data fusion

Yu-mei LIU1(),Ning-guo QIAO1,Jiao-jiao ZHUANG1,Peng-cheng LIU1,Ting HU1,Li-jun CHEN2()   

  1. 1. College of Transportation, Jilin University, Changchun 130022, China
    2. Big Data and Network Management Center, Jilin University, Changchun 130022, China
  • Received:2018-06-04 Online:2019-09-01 Published:2019-09-11
  • Contact: Li-jun CHEN E-mail:lymlls@163.com;clj@jlu.edu.cn

RICH HTML

 4   

PDF (PC)

107

Abstract:

Because the characteristics of many monitoring points, large data and low degree of data fusion of the gearbox in a rail vehicle, an anomaly detection method of the gearbox is proposed based on the correlation function fusion algorithm and fuzzy C means clustering. The correlation function fusion algorithm is employed to fuse the vibration signals collected from many monitoring points into a signal which can fully reflect the running state of the gearbox. The ensemble empirical mode decomposition (EEMD) method is applied to decompose the fused signal, and the energy entropy and singular entropy of the first n intrinsic mode functions (IMFs) are calculated. The fuzzy C means (FCM) clustering algorithm is employed to cluster the feature set to determine several abnormal situations in the gearbox. Through collection and analysis of the data during actual operation, the effectiveness of the proposed method is verified.

Key words: railway transportation, rail vehicle, anomaly detection of gearbox, correlation function fusion algorithm, fuzzy C means clustering

CLC Number: 

  • U260

Fig.1

Abnormal detection flow chart of gearbox in rail vehicle"

Fig.2

Exfoliation of outer ring of 1 axis output bearing"

Fig.3

Layout of sensors on gearbox"

Fig.4

Frequency?amplitude spectrums"

Table 1

Correlation coefficients between vibration"

测点 1 2 3 4
1 1 0.4910 0.4008 0.4184
2 0.4910 1 0.6951 0.4473
3 0.4008 0.6951 1 0.4679
4 0.4184 0.4473 0.4679 1

Fig.5

Fusion signal"

Fig.6

Frequency?amplitude spectrum of fusion signal"

Fig.7

EEMD entropy characteristics"

Fig.8

C=2-4, FCM clustering results"

1 袁雨青, 李强, 杨光, 等 . 高速列车齿轮箱线路测试与异常振动分析[J]. 铁道机车车辆, 2016, 36(1): 24-29.
Yuan Yu-qing , Li Qiang , Yang Guang , et al . Line test and abnormal vibration analysis of high speed train gearbox[J]. Railway Locomotive & Car, 2016, 36(1): 24-29.
2 雷亚国, 林京, 何正嘉 . 基于多传感器信息融合的行星齿轮箱故障诊断 [C]⫽2010年全国振动工程及应用学术会议,沈阳, 2010: 216-218.
3 Liu Y , Fan X , Lyv C , et al . An innovative information fusion method with adaptive kalman filter for integrated INS/GPS navigation of autonomous vehicles[J]. Mechanical Systems & Signal Processing, 2017, 100: 605-616.
4 徐旺敏, 张宇驰, 刘伍根, 等 . 基于相关函数的泄洪闸闸墩结构振动响应多测点融合研究[J]. 长江科学院院报, 2015, 32(11): 110-114.
Xu Wang-min , Zhang Yu-chi , Liu Wu-gen , et al . Research on multi point fusion of vibration response of floodgate pier structure based on correlation function[J]. Journal of Yangtze River Scientific Research Institute, 2015, 32(11): 110-114.
5 Liu Y M , Zhao C C , Xiong M Y , et al . Assessment of bearing performance degradation via extension and EEMD combined approach[J]. Journal of Central South University (English Edition), 2017, 24(5): 1155-1163.
6 贾凡, 严妍, 张家琪 . 基于K-means聚类特征消减的网络异常检测[J]. 清华大学学报: 自然科学版, 2018, 58(2): 137-142.
Jia Fan , Yan Yan , Zhang Jia-qi . K-means based feature reduction for network anomaly detection[J]. Journal of Tsinghua University (Science and Technology), 2018, 58(2): 137-142.
7 王军辉, 贾嵘, 谭泊 . 基于EEMD和模糊C均值聚类的风电机组齿轮箱故障诊断[J]. 太阳能学报, 2015, 36(2): 319-324.
Wang Jun-hui , Jia Rong , Tan bo . Fault diagnosis of wind turbine gearbox based on EEMD and fuzzy C means clustering[J]. Acta Energiae Solaris Sinica, 2015, 36(2): 319-324.
8 Wu Z , Huang N E . Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
9 Mok P Y , Huang H Q , Kwok Y L , et al . A robust adaptive clustering analysis method for automatic identification of clusters[J]. Pattern Recognition, 2012, 45(8): 3017-3033.
[1] Qing⁃ying LAI,Jun LIU,Ruo⁃yu ZHAO,Yong⁃ji LUO,Ling⁃yun MENG,Ya⁃zhi XU. Optimal trajectory planning for middle⁃to⁃high speed maglev based on dynamic programming with mutative spacing [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 749-756.
[2] YIN Zi-hong, ZHU Bo, SHAO Guo-xia, KONG De-hui, JIANG Liang-wei. Response of railway track and subgrade under the effect overweight goods [J]. 吉林大学学报(工学版), 2017, 47(5): 1446-1452.
[3] ZUO Jing, SHUAI Bin, HUANG Wen-cheng. Railway emergency rescue program searching based on improved distance entropy MULTIMOORA [J]. 吉林大学学报(工学版), 2017, 47(4): 1068-1074.
[4] NIU Zhi-hui, SU Jian, ZHANG Yi-rui, XU Guan, TAN Fu-xing. Track irregularity simulation based on bogie test rig [J]. 吉林大学学报(工学版), 2017, 47(2): 400-407.
[5] WANG Qi-ming, SU Jian, ZHANG Lan, CHEN Qiu-yu, XU Guan. Forward kinematics of orthogonal Stewart platform based on L-M algorithm [J]. 吉林大学学报(工学版), 2017, 47(1): 97-104.
[6] LIU Yu-mei, ZHAO Cong-cong, XIONG Ming-ye, GUO Wen-cui, ZHANG Zhi-yuan. Reliability assessment of high-speed railway drivetrain based on matter-element model [J]. 吉林大学学报(工学版), 2015, 45(4): 1063-1068.
[7] SHI Huai-long, SONG Ye, WU Ping-bo, ZENG Jing, ZHU Hai-yan. Calculation and testing of suspension stiffness of a bogie of high speed EMU [J]. 吉林大学学报(工学版), 2015, 45(3): 776-782.
[8] ZHANG Meng-nan,XU Hong-ze. Design of urban rail vehicle brake controller based on Krasovskii functionals [J]. 吉林大学学报(工学版), 2015, 45(1): 104-111.
[9] WANG Xiu-gang, SU Jian, CAO Xiao-ning, XU Zhen, LU Hai-ge, TIAN Zong-ju. Forward kinematics solution of bogie 6-DOF platform based on the orthogonality of rotation matrix [J]. 吉林大学学报(工学版), 2013, 43(05): 1241-1246.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd