|Table of Contents|

Fault feature analysis of high-speed train bogie based on empirical mode decomposition entropy(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2014年01期
Page:
57-
Research Field:
载运工具运用工程
Publishing date:

Info

Title:
Fault feature analysis of high-speed train bogie based on empirical mode decomposition entropy
Author(s):
QIN Na1;  WANG Kai-yun2;  JIN Wei-dong1;  HUANG Jin1;  SUN Yong-kui1
1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, China; 2. State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, Sichuan, China
Keywords:
high-speed train;  fault diagnosis;  feature extraction;  ensemble empirical mode decomposition;  entropy;  empirical mode entropy
PACS:
U279.3
DOI:
-
Abstract:
A novel method of feature extraction was proposed by combining ensemble empirical mode decomposition(EEMD)and five entropies based on the characteristics of vibration signal for high-speed train bogie in failure station. Firstly, vibration signal was decomposed by EEMD to avoid mode mixing effectively. Secondly, EEMD entropy feature was calculated for describing the complexity of intrinsic mode functions(IMFs). Vibration signals were obtained under four typical working conditions including normal condition, air spring fault, lateral damper fault and yaw damper fault. There were 280 sample data including 60% training samples and 40% test samples. Analysis result shows that the method is good adaptivity for unselecting basis functions and decomposition levels. The recognition rate is above 95% at the running speed of 200 km·h-1. Therefore, the feature extraction method is effective to analyze the vibration signal of high-speed train bogie in fault station. 3 tabs, 11 figs, 15 refs.

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Last Update: 2014-03-20