Home Journals Articles Updates About
CN
image
收藏切换
Compound fault diagnosis method of rolling bearings based on IDBO-TVFEMD and improved wavelet threshold function
收藏切换
PDF
Fengfeng BIE1, 2, Yuting ZHANG1, 2, Qianqian LI1, 2, Xueping DING1, 2, Guangcheng PENG3, Yuxuan DAI1, 2, Hanyang ZHANG1, 2
Journal of Mechanical Strength | 2025, 47(10) : 51 - 62
Less
收藏切换
Journal of Mechanical Strength | 2025, 47(10): 51-62
• Vibration·Noise·Monitoring·Diagnosis •
Compound fault diagnosis method of rolling bearings based on IDBO-TVFEMD and improved wavelet threshold function
Full
Fengfeng BIE1, 2, Yuting ZHANG1, 2, Qianqian LI1, 2, Xueping DING1, 2, Guangcheng PENG3, Yuxuan DAI1, 2, Hanyang ZHANG1, 2
Affiliations
  • 1.School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
  • 2.Jiangsu Key Laboratory of Green Process Equipment, Changzhou University, Changzhou 213164, China
  • 3.Yancheng Chongda Petrochemical Machinery Co., Ltd., Yancheng 224712, China
Published: 2025-10-15 doi: 10.16579/j.issn.1001.9669.2025.10.006
Outline
收藏切换

A fault diagnosis method based on improved dung beetle optimizer (IDBO)-time varying filtered empirical mode decomposition (TVFEMD) with improved wavelet threshold functions was proposed aiming at that the vibration signal of rolling bearing fault tends to be disturbed and overwhelmed by strong noise background. IDBO was primarily developed to iteratively optimize B-spline order and bandwidth threshold ξ in TVFEMD，and the optimal parameter combination was obtained. Applying TVFEMD on the original signal, the decomposition for intrinsic mode function (IMF) component series were achieved, among which the irrelevant components were removed by correlation coefficient criterion, and target signals were reconstructed. Then the improved wavelet threshold function was employed on the new signal for further denoising.Finally, the envelope spectrum of the processed signal was calculated, from which the typical fault characteristic frequency was extracted. Through simulation signal and fault simulation test analysis, the fault diagnosis method combined with IDBO-TVFEMD and improved wavelet threshold function was compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and complete EEMD with adaptive noise (CEEMDAN) denoising methods. The research results show that the algorithm model proposed in this paper has higher efficiency.

Rolling bearing  /  Time varying filtered empirical mode decomposition  /  Dung beetle optimizer algorithm  /  Wavelet threshold function
Fengfeng BIE, Yuting ZHANG, Qianqian LI, Xueping DING, Guangcheng PENG, Yuxuan DAI, Hanyang ZHANG. Compound fault diagnosis method of rolling bearings based on IDBO-TVFEMD and improved wavelet threshold function[J]. Journal of Mechanical Strength, 2025 , 47 (10) : 51 -62 . DOI: 10.16579/j.issn.1001.9669.2025.10.006
Funding
Less
  • National Natural Science Foundation of China(52206041)
  • Major Project of Jiangsu Provincial Department of Education(19KJA430004)
  • Jiangsu Graduate Research and Practice Innovation Program(SJCX23_1503)
  • Project Results of Innovation and Entrepreneurship Training Plan for College Students in Jiangsu Province(202310292018Z)
Appendix
Less
Year 2025 volume 47 Issue 10
PDF
136
64
Cite this Article
BibTeX
Article Info
doi: 10.16579/j.issn.1001.9669.2025.10.006
  • Receive Date：2024-01-17
  • Online Date：2026-02-11
  • Published：2025-10-15
Article Data
Affiliations
History
  • Received：2024-01-17
  • Revised：2024-04-25
Funding
National Natural Science Foundation of China(52206041)
Major Project of Jiangsu Provincial Department of Education(19KJA430004)
Jiangsu Graduate Research and Practice Innovation Program(SJCX23_1503)
Project Results of Innovation and Entrepreneurship Training Plan for College Students in Jiangsu Province(202310292018Z)
Affiliations
    1.School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
    2.Jiangsu Key Laboratory of Green Process Equipment, Changzhou University, Changzhou 213164, China
    3.Yancheng Chongda Petrochemical Machinery Co., Ltd., Yancheng 224712, China

Corresponding:

ZHANG Yuting, E-mail:
References
Share
https://castjournals.cast.org.cn/joweb/jxqd/EN/10.16579/j.issn.1001.9669.2025.10.006
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House