Gearbox fault diagnosis of high-speed railway train

被引：30

作者：

Zhang, Bing ^{[1
]}

Tan, Andy C. C. ^{[2
,3
]}

Lin, Jian-hui ^{[1
]}

机构：

[1] Southwest Jiaotong Univ, State Key Lab Tract Power, Chengdu 610031, Sichuan, Peoples R China

[2] Queensland Univ Technol, Fac Sci & Engn, 2 George St, Brisbane, Qld 4001, Australia

[3] Univ Tunku Abdul Rahman, LKC Fac Engn & Sci, Cheras 43000, Kajang, Malaysia

来源：

ENGINEERING FAILURE ANALYSIS | 2016年 / 66卷

关键词：

High-speed railway; Gear box; Vibration test; Modal analysis; Fault diagnosis; Finite element analysis; VIBRATION; FREQUENCY;

D O I：

10.1016/j.engfailanal.2016.04.020

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

This paper is aimed at detecting crack faults of a lubricant level viewport and upper viewport of a high-speed railway train gear box operating for a repair cycle from 200,000 to 300,000 km. This paper reports a series of test results involving vibration and dynamic stress tests to identify the occurrence of partial resonance and stress concentration points of the gear box structure under external excitation tests. The results of the tests were confirmed using both finite element analysis and modal analysis. This paper also describes the causes of the crack fault on the gear box body and provides a comprehensive analysis of the causes of the gearbox crack. With this approach the faults on the high-speed train gearbox structure were identified and corrective solutions suggested. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：407 / 420

页数：14

共 50 条

[21] Machine learning for fault diagnosis of high-speed train traction systems: A review
Huan Wang
Yan-Fu Li
Jianliang Ren
[J]. Frontiers of Engineering Management, 2024, 11 : 62 - 78
[22] Fault Diagnosis of High-speed Train Bogie Based on Deep Neural Network
Zhang, Yuanjie
Qin, Na
Huang, Deqing
Liang, Kaiwei
[J]. IFAC PAPERSONLINE, 2019, 52 (24): : 135 - 139
[23] Fault Diagnosis of High-Speed Train Bogie by Residual-Squeeze Net
Su, Liyuan
Ma, Lei
Qin, Na
Huang, Deqing
Kemp, Andrew H.
[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2019, 15 (07) : 3856 - 3863
[24] An Adaptive Multisensor Fault Diagnosis Method for High-Speed Train Traction Converters
Dong, Honghui
Chen, Fuzhao
Wang, Zhipeng
Jia, Limin
Qin, Yong
Man, Jie
[J]. IEEE TRANSACTIONS ON POWER ELECTRONICS, 2021, 36 (06) : 6288 - 6302
[25] Fault diagnosis of high-speed train bogie based on LSTM neural network
Deqing HUANG
Yuanzhe FU
Na QIN
Shibin GAO
[J]. Science China(Information Sciences), 2021, 64 (01) : 260 - 262
[26] Vehicular Edge Computing Model for Fault Detection and Diagnosis of High-Speed Train
Liu, Yuxin
Lin, Siyu
Li, Wenjie
Zhong, Zhangdui
[J]. 2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2020,
[27] Machine learning for fault diagnosis of high-speed train traction systems: A review
Wang, Huan
Li, Yan-Fu
Ren, Jianliang
[J]. FRONTIERS OF ENGINEERING MANAGEMENT, 2024, 11 (01) : 62 - 78
[28] Fault diagnosis of high-speed train bogie based on LSTM neural network
Huang, Deqing
Fu, Yuanzhe
Qin, Na
Gao, Shibin
[J]. SCIENCE CHINA-INFORMATION SCIENCES, 2021, 64 (01)
[29] Fault Diagnosis of High-Speed Railway Turnout Based on Convolutional Neural Network
Zhang, Peng
Zhang, Guohua
Dong, Wei
Sun, Xinya
Ji, Xingquan
[J]. 2018 24TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND COMPUTING (ICAC' 18), 2018, : 719 - 724
[30] Fault Diagnosis of High-Speed Railway Turnout Based on Support Vector Machine
Zhou, Fenfang
Xia, Li
Dong, Wei
Sun, Xinya
Yan, Xiang
Zhao, Qianchuan
[J]. PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), 2016, : 1539 - 1544

← 1 2 3 4 5 →