Data Fusion Generative Adversarial Network for Multi-Class Imbalanced Fault Diagnosis of Rotating Machinery

Data Fusion Generative Adversarial Network for Multi-Class Imbalanced Fault Diagnosis of Rotating Machinery

For the fault diagnosis problems of rotating machinery in the real industrial practice, measurement data with imbalanced class distributions negatively affect the diagnostic performance of most conventional machine learning classification algorithms since equal cost weights are assigned to different...

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Bibliographic Details
Main Authors: Qianjun Liu, Guijun Ma, Cheng Cheng
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Generative adversarial networks
imbalanced fault diagnosis
data continuity
rotating machinery
Online Access:https://ieeexplore.ieee.org/document/9058665/

Internet

https://ieeexplore.ieee.org/document/9058665/

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