A Deep Transfer Model With Wasserstein Distance Guided Multi-Adversarial Networks for Bearing Fault Diagnosis Under Different Working Conditions

A Deep Transfer Model With Wasserstein Distance Guided Multi-Adversarial Networks for Bearing Fault Diagnosis Under Different Working Conditions

In recent years, intelligent fault diagnosis technology with the deep learning algorithm has been widely used in the manufacturing industry for substituting time-consuming human analysis method to enhance the efficiency of fault diagnosis. The rolling bearing as the connection between the rotor and...

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Bibliographic Details
Main Authors:	Ming Zhang, Duo Wang, Weining Lu, Jun Yang, Zhiheng Li, Bin Liang
Format:	Article
Language:	English
Published:	IEEE 2019-01-01
Series:	IEEE Access
Subjects:	Transfer learning fault diagnosis convolutional neural network multi-adversarial networks
Online Access:	https://ieeexplore.ieee.org/document/8713860/

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