|Table of Contents|

Fault diagnosis method of railway vehicle with wheel flat based on self-adaptive multi-scale morphology analysis

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

Issue:
2015年01期
Page:
58-65
Research Field:
载运工具运用工程
Publishing date:

Info

Title:
Fault diagnosis method of railway vehicle with wheel flat based on self-adaptive multi-scale morphology analysis
Author(s):
LI Yi-fan1;  LIU Jian-xin2;  LIN Jian-hui2;  LI Zhong-ji3
1. E’mei Campus Department of Mechanical Engineering, Southwest Jiaotong University, E’mei 614202,Sichuan, China; 2. State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031,Sichuan, China; 3. Institute of Science and Technology, China Railway Eryuan Engineering GroupCo., Ltd., Chengdu 610031, Sichuan, China
Keywords:
vehicle engineering;  wheel flat;  fault diagnosis;  multi-scale morphology filter
PACS:
U270.331.1
DOI:
-
Abstract:
A vehicle system dynamics model with 56 degrees of freedom and a wheel flat model were set up to calculate railway vehicle dynamic responses. The vibration information of vehicle was often influenced by various interferences, such as track irregularity and vehicle speed alteration. In order to effectively extract the wheel-track impact features from strong background noises, a self-adaptive multi-scale morphology filtering analysis algorithm was proposed to study the axle box vibration characteristics caused by wheel flat. The influences of track irregularity and vehicle running speed on the fault diagnosis result of axle box were discussed. Simulation result shows that the fault frequencies of 10, 15, 20 Hz are obtained by using morphology filter based on 7-scale and 9-scale structural elements at the speeds of 100, 150, 200 km·h-1 with the American fifth grade and third grade track irregularities. Test result demonstrates that the fault frequency of 2 Hz is obtained by using morphology filter based on 7-scale structural element at the speed of 40 km·h-1, which is corresponding to the theoretic frequency of wheel flat, so diagnosis result is reliable. 10 figs, 19 refs.

References:

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Last Update: 2015-02-25