Deep Learning Network Based on Improved Sparrow Search Algorithm Optimization for Rolling Bearing Fault Diagnosis

Guoyuan Ma, Xiaofeng Yue (), Juan Zhu, Zeyuan Liu and Shibo Lu
Additional contact information
Guoyuan Ma: Mechanical and Electrical Engineering, Changchun University of Technology, Yan’an Avenue, Changchun 130012, China
Xiaofeng Yue: Mechanical and Electrical Engineering, Changchun University of Technology, Yan’an Avenue, Changchun 130012, China
Juan Zhu: Mechanical and Electrical Engineering, Changchun University of Technology, Yan’an Avenue, Changchun 130012, China
Zeyuan Liu: Mechanical and Electrical Engineering, Changchun University of Technology, Yan’an Avenue, Changchun 130012, China
Shibo Lu: Mechanical and Electrical Engineering, Changchun University of Technology, Yan’an Avenue, Changchun 130012, China

Mathematics, 2023, vol. 11, issue 22, 1-20

Abstract: In recent years, deep learning has been increasingly used in fault diagnosis of rotating machinery. However, the actual acquisition of rolling bearing fault signals often contains ambient noise, making it difficult to determine the optimal values of the parameters. In this paper, a sparrow search algorithm (LSSA) based on backward learning of lens imaging and Gaussian Cauchy variation is proposed. The lens imaging reverse learning strategy enhances the traversal capability of the algorithm and allows for a better balance of algorithm exploration and development. Then, the performance of the proposed LSSA was tested on the benchmark function. Finally, LSSA is used to find the optimal modal component K and the optimal penalty factor α in VMD-GRU, which in turn realizes the fault diagnosis of rolling bearings. The experimental results show that the model can achieve a 96.61% accuracy in rolling bearing fault diagnosis, which proves the effectiveness of the method.

Keywords: sparrow search algorithm; deep learning; fault detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/22/4634/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/22/4634/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:22:p:4634-:d:1279343

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().