Improved SE-ResNet Acoustic–Vibration Fusion for Rolling Bearing Composite Fault Diagnosis

Improved SE-ResNet Acoustic–Vibration Fusion for Rolling Bearing Composite Fault Diagnosis

An enhanced fault diagnosis approach for rolling bearings with composite faults using an optimized Squeeze and Excitation ResNet (SE-ResNet) model is proposed. This method integrates grid search (GS), support vector regression (SVR), ensemble empirical mode decomposition (EEMD), and low-rank multimo...

Full description

Bibliographic Details
Main Authors: Xiaojiao Gu, Yang Tian, Chi Li, Yonghe Wei, Dashuai Li
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Applied Sciences
Subjects:
deep learning
fault diagnosis
neural network
ensemble empirical mode decomposition
low-rank multimodal fusion
Online Access:https://www.mdpi.com/2076-3417/14/5/2182

Internet

https://www.mdpi.com/2076-3417/14/5/2182

Similar Items