Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis

Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis

In this study, we developed a novel data-driven fault detection and diagnosis (FDD) method for bearing faults in induction motors where the fault condition data are imbalanced. First, we propose a bearing fault detector based on convolutional neural networks (CNN), in which the vibration signals fro...

Full description

Bibliographic Details
Main Authors: Sungho Suh, Haebom Lee, Jun Jo, Paul Lukowicz, Yong Oh Lee
Format: Article
Language:English
Published: MDPI AG 2019-02-01
Series:Applied Sciences
Subjects:
generative adversarial networks
oversampling
data imbalance problem
bearing fault
convolutional neural networks
Online Access:https://www.mdpi.com/2076-3417/9/4/746

Internet

https://www.mdpi.com/2076-3417/9/4/746

Similar Items