Fault diagnosis in rolling bearings using multi-gaussian attention and covariance loss for single domain generalization

This article introduces a new method for fault diagnosis in rolling bearings, addressing performance drops caused by domain shifts under changing operational conditions. Unlike domain generalization (DG), which relies on multiple source domains, this method focuses on single DG to learn robust featu...

Full description

Saved in:
Bibliographic Details
Main Authors: Yan Song, Yinghao Zhuang, Daichao Wang, Yibin Li, Eve Zhang
Format: Default Article
Published: 2025
Subjects:
Engineering
Communications engineering
Electrical engineering
Electronics, sensors and digital hardware
Other physical sciences
Electrical & Electronic Engineering
Feature extraction
Fault diagnosis
Training
Adaptation models
Data models
Testing
Rolling bearings
Convolution
Accuracy
Vibrations
Covariance loss
Intelligent fault diagnosis
Multi-gaussian attention
Single domain generalization (SDG)
Online Access:https://hdl.handle.net/2134/28990850.v1
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items