Dr. Ming Zhang

Dr. Ming Zhang

Research Associate, College Of Engineering And Physical Sci, Aston University, Birmingham, UK

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

Published in IEEE Access, 2019

Recommended citation: M. Zhang, D. Wang, W. Lu, J. Yang, Z. Li and B. Liang, "A Deep Transfer Model With Wasserstein Distance Guided Multi-Adversarial Networks for Bearing Fault Diagnosis Under Different Working Conditions," in IEEE Access, vol. 7, pp. 65303-65318, 2019, doi: 10.1109/ACCESS.2019.2916935. https://doi.org/10.1109/ACCESS.2019.2916935

Any one is interested in this work could find the code in my respostory Transfer-Learning-for-Fault-Diagnosis

Download paper here

% Recommended citation: M. Zhang, D. Wang, W. Lu, J. Yang, Z. Li and B. Liang, “A Deep Transfer Model With Wasserstein Distance Guided Multi-Adversarial Networks for Bearing Fault Diagnosis Under Different Working Conditions,” in IEEE Access, vol. 7, pp. 65303-65318, 2019, doi: 10.1109/ACCESS.2019.2916935.