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

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 al...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) Vol. 11; no. 22; p. 4634
Main Authors: Ma, Guoyuan, Yue, Xiaofeng, Zhu, Juan, Liu, Zeyuan, Lu, Shibo
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2023
Subjects:
Accuracy
Algorithms
Bearings
Deep learning
fault detection
Fault diagnosis
Food
Lagrange multiplier
Lenses
Methods
Neural networks
Roller bearings
Rotating machinery
Search algorithms
Signal processing
sparrow search algorithm
Spectrum analysis
ISSN:2227-7390, 2227-7390
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2227-7390
2227-7390
DOI:10.3390/math11224634