An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis

An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis

Abstract As a critical component of rotating machinery, the operating status of rolling bearings is not only related to significant economic interests but also has a far-reaching impact on social security. Hence, ensuring an effective diagnosis of faults in rolling bearings is paramount in maintaini...

Full description

Saved in:
Bibliographic Details
Main Authors: Hao Yan, Liangliang Shang, Wan Chen, Mengyao Jiang, Tianqi lu, Fei Li
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
Rolling bearings
Fault diagnosis
Stacked denoising autoencoders
Extreme learning machine
Aquila optimizer algorithm
Online Access:https://doi.org/10.1038/s41598-025-94703-w
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items