Home Journals Articles Updates About
CN
image
收藏切换
A feature extraction method based on improved resonance sparse decomposition for early faults in rolling bearings
收藏切换
PDF
Meng SUN, Bingpeng GAO, Jing CHENG
Journal of Mechanical Strength | 2025, 47(6) : 17 - 26
Less
收藏切换
Journal of Mechanical Strength | 2025, 47(6): 17-26
• Vibration·Noise·Monitoring·Diagnosis •
A feature extraction method based on improved resonance sparse decomposition for early faults in rolling bearings
Full
Meng SUN, Bingpeng GAO, Jing CHENG
Affiliations
  • School of Electrical Engineering, Xinjiang University, Urumqi 830017, China
Published: 2025-06-15 doi: 10.16579/j.issn.1001.9669.2025.06.003
Outline
收藏切换

To overcome the difficulty in early fault diagnosis with weak fault characteristics of rolling bearings that are easily drowned out by noise in the complex operation environment, an early fault diagnosis method was proposed by integrating the improved artificial gorilla troops optimizer (IGTO) algorithm, the optimized resonance-based sparse signal decomposition (RSSD), multi-parameter and sparse maximum harmonics-to-noise-ratio deconvolution (SMHD) method. Firstly, taking the squared envelope spectrum correlated kurtosis (SE-SCK) negative value of the low resonance component as the objective function, IGTO was used to simultaneously optimize the quality factor Q, weight coefficient λ and Lagrange multiplier μ of RSSD, for the achievement of the optimal matching of wavelet basis function and dissipation function. Secondly, the obtained optimal low resonance component was inputed into SMHD for filtering processing. Finally, the fault features were extracted by the perform envelope spectrum analysis. The algorithm comparison experiments show that the proposed IGTO algorithm has significantly improved optimization performance. The results of simulation and XJTU-SY bearing full life cycle fault signal test show that the proposed method is more useful in extracting early weak fault characteristics of bearings.

Improved artificial gorilla troops algorithm  /  Resonance sparse decomposition  /  Square envelope spectrum correlation kurtosis  /  Sparse maximum harmonics-to-noise-ratio deconvolution  /  Early fault diagnosis
Meng SUN, Bingpeng GAO, Jing CHENG. A feature extraction method based on improved resonance sparse decomposition for early faults in rolling bearings[J]. Journal of Mechanical Strength, 2025 , 47 (6) : 17 -26 . DOI: 10.16579/j.issn.1001.9669.2025.06.003
Funding
Less
  • National Key Research and Development Program of China(2021YFB1506902)
  • Fundamental Research Funds for Universities of Xinjiang Uygur Autonomous Region(XJEDU2023P025)
Appendix
Less
Year 2025 volume 47 Issue 6
PDF
77
37
Cite this Article
BibTeX
Article Info
doi: 10.16579/j.issn.1001.9669.2025.06.003
  • Receive Date：2023-09-08
  • Online Date：2026-03-18
  • Published：2025-06-15
Article Data
Affiliations
History
  • Received：2023-09-08
  • Revised：2023-11-30
Funding
National Key Research and Development Program of China(2021YFB1506902)
Fundamental Research Funds for Universities of Xinjiang Uygur Autonomous Region(XJEDU2023P025)
Affiliations
    School of Electrical Engineering, Xinjiang University, Urumqi 830017, China

Corresponding:

GAO Bingpeng, E-mail：
References
Share
https://castjournals.cast.org.cn/joweb/jxqd/EN/10.16579/j.issn.1001.9669.2025.06.003
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