Cross-Domain Fault Diagnosis of Rotating Machinery Using Discriminative Feature Attention Network

Cross-Domain Fault Diagnosis of Rotating Machinery Using Discriminative Feature Attention Network

In recent industrial applications, machine learning technology is proving useful in preventing equipment failures in advance through early failure diagnosis. In particular, we show that different domains can be linked through adversarial learning with data available in different working conditions t...

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Bibliographic Details
Main Authors: Gye-Bong Jang, Jin-Young Kim, Sung-Bae Cho
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Attention mechanism
domain adaptation
fault diagnosis
reliability
rotating machines
vibration measurement
Online Access:https://ieeexplore.ieee.org/document/9478860/

Internet

https://ieeexplore.ieee.org/document/9478860/

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