An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks

An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks

To address the issue of rolling bearing fault diagnosis, this paper proposes a novel model combining the Improved Gorilla Troop Optimization (IGTO) algorithm, Variational Mode Decomposition (VMD), Permutation Entropy (PE), and Long Short-Term Memory (LSTM) networks. The IGTO algorithm is used to opt...

Full description

Saved in:
Bibliographic Details
Main Authors: Xianglong Luo, Fengrong Yu, Jing Qian, Biao An, Nengpeng Duan
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Applied Sciences
Subjects:
rolling bearing
fault diagnosis
improved gorilla troops optimization
variational mode decomposition
long-term memory network
Online Access:https://www.mdpi.com/2076-3417/15/8/4338
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2076-3417/15/8/4338

Similar Items