Fault diagnosis of rolling bearing based on time and frequency domain analysis and EMD

被引：0

作者：

Zhu, Liandie ^{[1
]}

Dai, Wei ^{[1
]}

Liu, Guixiu ^{[2
]}

Du, Rui ^{[2
]}

机构：

[1] Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China

[2] Nanjing Chenguang Grp Co Ltd, Nanjing, Peoples R China

来源：

2019 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-QINGDAO) | 2019年

关键词：

component; PIIM; Time domain analysis; Frequency domain analysis; Empirical Mode Decomposition; feature extraction;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Prognostic and health management (PHM) technology is the use of a large amount of condition monitoring data and information, with the help of all kinds of fault model and artificial intelligence algorithms monitoring, diagnosis, prediction and management of the health status of the equipment technology, by predicting the problems and reliable working life, improving the safety of equipment, minimizing the fault effect, this article in rolling bearing, using Labview software construction time domain analysis program, the analysis of three kinds of condition from different perspective (35Hz12KN/37.5Hz11KN/40HZ10KN)under the rolling bearing, Finally, Matlab software was used for frequency domain analysis and empirical mode decomposition (EMD), and the inherent modal function and vibration signal spectrum were extracted to find out the fault characteristic frequency band, which provided a basis for bearing fault diagnosis under different loads.

引用

页数：6

共 50 条

[21] Rolling bearing fault diagnosis based on manifold feature domain adaptation
Zhou H.
Huang T.
Li Z.
Zhong F.
[J]. Zhendong yu Chongji/Journal of Vibration and Shock, 2024, 43 (05): : 94 - 102
[22] Comparison of EMD and EEMD in rolling bearing fault signal analysis
Fang, Ke
Zhang, Hesheng
Qi, Hongmei
Dai, Yimei
[J]. 2018 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC): DISCOVERING NEW HORIZONS IN INSTRUMENTATION AND MEASUREMENT, 2018, : 737 - 741
[23] Fault Detection of Rolling Bearing Based on EMD-DPCA
Jiang, Liying
Gong, Guangting
Zhang, Yanpeng
Liu, Zhipeng
Cui, Jianguo
[J]. 26TH CHINESE CONTROL AND DECISION CONFERENCE (2014 CCDC), 2014, : 3207 - 3211
[24] Fault Diagnose of Rolling Bearing Based Order Bispectrum and EMD
Chen Yingtao
Kang Haiying
[J]. DISCOVERY, INNOVATION AND COMMUNICATION - 5TH CSAA SCIENCE AND TECHNIQUE YOUTH FORUM, 2012, : 471 - 475
[25] Fault diagnosis method of rolling bearing based on EMD-Hilbert envelope spectrum and BPNN
Wu, Tao
[J]. 2020 ASIA CONFERENCE ON GEOLOGICAL RESEARCH AND ENVIRONMENTAL TECHNOLOGY, 2021, 632
[26] Rolling Bearing Fault Diagnosis Using Sample Entropy and 1.5 Dimension Spectrum Based on EMD
Zhong, Xianyou
Zhao, Chunhua
Dong, Haijiang
Liu, Xianming
Zeng, Liangcai
[J]. ADVANCES IN MECHATRONICS AND CONTROL ENGINEERING, PTS 1-3, 2013, 278-280 : 1027 - +
[27] Fault Diagnosis of Rolling Bearing based on EMD Combined with HHT Envelope and Wavelet Spectrum Transform
Ma Yabin
Chen Chen
Shu Qiqi
Wang Jian
Liu Hongliang
Huang Darong
[J]. PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS), 2018, : 481 - 485
[28] The Diagnosis Approach for Rolling Bearing Fault based on Kurtosis Criterion EMD and Hilbert Envelope Spectrum
Luo, Cheng
Jia, MinPing
Wen, Yue
[J]. 2017 IEEE 3RD INFORMATION TECHNOLOGY AND MECHATRONICS ENGINEERING CONFERENCE (ITOEC), 2017, : 692 - 696
[29] Fault Diagnosis of Hoist Gearbox Based on Time-Domain Analysis of EMD and Fuzzy Clustering
Li, Zigui
Yan, Bijuan
[J]. MECHATRONICS AND MATERIALS PROCESSING I, PTS 1-3, 2011, 328-330 : 1717 - 1720
[30] Fault diagnosis of rolling element bearing based on a new noise-resistant time-frequency analysis method
Wang, Hongchao
Hao, Fang
[J]. JOURNAL OF VIBROENGINEERING, 2018, 20 (08) : 2825 - 2838

← 1 2 3 4 5 →