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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Veröffentlicht in:	IEEE access 2019, Vol.7, p.65303-65318
Hauptverfasser:	Zhang, Ming, Wang, Duo, Lu, Weining, Yang, Jun, Li, Zhiheng, Liang, Bin
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Convolution convolutional neural network Data models Domains Employee welfare Fault diagnosis Machine learning multi-adversarial networks Networks Performance degradation Roller bearings Rolling bearings Training Transfer learning Wavelength division multiplexing Working conditions
Online-Zugang:	Volltext
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