Rolling Bearing Fault Diagnosis Based on Improved GAN and 2-D Representation of Acoustic Emission Signals

Rolling Bearing Fault Diagnosis Based on Improved GAN and 2-D Representation of Acoustic Emission Signals

Bearing fault diagnosis is essential in manufacturing systems to avoid problems such as downtime costs. Convolutional neural network (CNN) models have enabled a new generation of intelligent bearing fault diagnosis methods for smart manufacturing owing to their capability to extract features for 2-d...

Full description

Bibliographic Details
Main Authors: Minh Tuan Pham, Jong-Myon Kim, Cheol Hong Kim
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Acoustic emission
bearing fault diagnosis
convolutional neural network
GAN
unbalance data
Online Access:https://ieeexplore.ieee.org/document/9837015/

Internet

https://ieeexplore.ieee.org/document/9837015/

Similar Items