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

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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

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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"

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