Wheel-bearing fault diagnosis of trains using empirical wavelet transform

被引：208

作者：

Cao, Hongrui ^{[1
]}

Fan, Fei ^{[1
]}

Zhou, Kai ^{[1
]}

He, Zhengjia ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, State Key Lab Mfg & Syst Engn, Xian 710049, Peoples R China

来源：

MEASUREMENT | 2016年 / 82卷

基金：

中国国家自然科学基金;

关键词：

Wheel-bearing; Vibration signal; Empirical wavelet transform; Faults diagnosis; MODE DECOMPOSITION; ROTATING MACHINERY; SIGNAL ANALYSIS; VIBRATION; FREQUENCY;

D O I：

10.1016/j.measurement.2016.01.023

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Rolling bearings are used widely as wheel bearing in trains. Fault detection of the wheelbearing is of great significance to maintain the safety and comfort of train. Vibration signal analysis is the most popular technique that is used for rolling element bearing monitoring, however, the application of vibration signal analysis for wheel bearings is quite limited in practice. In this paper, a novel method called empirical wavelet transform (EWT) is used for the vibration signal analysis and fault diagnosis of wheel-bearing. The EWT method combines the classic wavelet with the empirical mode decomposition, which is suitable for the non-stationary vibration signals. The effectiveness of the method is validated using both simulated signals and the real wheel-bearing vibration signals. The results show that the EWT provides a good performance in the detection of outer race fault, roller fault, and the compound fault of outer race and roller. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：439 / 449

页数：11

共 50 条

[31] Fault Diagnosis of Rolling Bearing Based on Wavelet Package Transform and Ensemble Empirical Mode Decomposition
Liu, Quan
Chen, Fen
Zhou, Zude
Wei, Qin
ADVANCES IN MECHANICAL ENGINEERING, 2013,
[32] Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform
Kankar, P. K.
Sharma, Satish C.
Harsha, S. P.
JOURNAL OF VIBRATION AND CONTROL, 2011, 17 (14) : 2081 - 2094
[33] A Novel Rolling Bearing Fault Diagnosis Method Based on Empirical Wavelet Transform and Spectral Trend
Xu, Yonggang
Deng, Yunjie
Zhao, Jiyuan
Tian, Weikang
Ma, Chaoyong
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2020, 69 (06) : 2891 - 2904
[34] Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform
Kankar, P. K.
Sharma, Satish C.
Harsha, S. P.
NEUROCOMPUTING, 2013, 110 : 9 - 17
[35] Ball Bearing Fault Diagnosis Using Wavelet Transform and Principal Component Analysis
Kamiel, Berli Paripurna
Howard, Ian
INNOVATIVE SCIENCE AND TECHNOLOGY IN MECHANICAL ENGINEERING FOR INDUSTRY 4.0, 2019, 2187
[36] Bearing Fault Diagnosis Using Discrete Wavelet Transform And Artificial Neural Network
Patil, Aditi B.
Gaikwad, Jitendra A.
Kulkarni, Jayant V.
PROCEEDINGS OF THE 2016 2ND INTERNATIONAL CONFERENCE ON APPLIED AND THEORETICAL COMPUTING AND COMMUNICATION TECHNOLOGY (ICATCCT), 2016, : 399 - 405
[37] A fault diagnosis method for roller bearing based on empirical wavelet transform decomposition with adaptive empirical mode segmentation
Song, Yueheng
Zeng, Shengkui
Ma, Jiming
Guo, Jianbin
MEASUREMENT, 2018, 117 : 266 - 276
[38] Bearing and gear fault diagnosis using adaptive wavelet transform of vibration signals
Jena, D. P.
Panigrahi, S. N.
INTERNATIONAL CONFERENCE ON ADVANCES SCIENCE AND CONTEMPORARY ENGINEERING 2012, 2012, 50 : 265 - 274
[39] Detection and diagnosis of fault bearing using wavelet packet transform and neural network
Said, Djaballah
Kamel, Meftah
Khaled, Khelil
Mohsein, Tedjini
Lakhdar, Sedira
FRATTURA ED INTEGRITA STRUTTURALE, 2019, 13 (49): : 291 - 301
[40] Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform
Hsueh, Yu-Min
Ittangihal, Veeresh Ramesh
Wu, Wei-Bin
Chang, Hong-Chan
Kuo, Cheng-Chien
SYMMETRY-BASEL, 2019, 11 (10):

← 1 2 3 4 5 →