Rolling Bearing Fault Diagnosis Based on SCNN and Optimized HKELM
The issue of insufficient multi-scale feature extraction and difficulty in accurately classifying fault features in rolling bearing fault diagnosis is addressed by proposing a novel diagnostic method that integrates stochastic convolutional neural networks (SCNNs) and a hybrid kernel extreme learnin...
Saved in:
| Main Authors: | Yulin Wang, Xianjun Du |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/12/2004 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Bearing Fault Diagnosis Based on IPOA-VMD and SSA-HKELM
by: Baoxian Chang, et al.
Published: (2024-01-01) -
Research on bearing fault diagnosis based on ISA-VMD and IMSE
by: Feng Yan
Published: (2025-04-01) -
A review on multi-fidelity hyperparameter optimization in machine learning
by: Jonghyeon Won, et al.
Published: (2025-04-01) -
An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis
by: Hao Yan, et al.
Published: (2025-04-01) -
Multi-strategy enhanced artificial rabbits optimization for prediction of grades in tourism service communication courses
by: Xiaodan Qu, et al.
Published: (2025-07-01)