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...

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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

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