Deep Adversarial Domain Adaptation Model for Bearing Fault Diagnosis

Fault diagnosis of rolling bearings is an essential process for improving the reliability and safety of the rotating machinery. It is always a major challenge to ensure fault diagnosis accuracy in particular under severe working conditions. In this article, a deep adversarial domain adaptation (DADA...

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Veröffentlicht in:	IEEE transactions on systems, man, and cybernetics. Systems Jg. 51; H. 7; S. 4217 - 4226
Hauptverfasser:	Liu, Zhao-Hua, Lu, Bi-Liang, Wei, Hua-Liang, Chen, Lei, Li, Xiao-Hua, Ratsch, Matthias
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Adaptation Adversarial network Algorithms bearing Classification Data mining Data models Deep learning deep neural networks domain adaptation (DA) Domains Fault diagnosis Feature extraction Machine learning Roller bearings Rolling bearings Rotating machinery stack autoencoder (SAE) Training unsupervised learning
ISSN:	2168-2216, 2168-2232
Online-Zugang:	Volltext
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