Generalization of Deep Neural Networks for Imbalanced Fault Classification of Machinery Using Generative Adversarial Networks

Generalization of Deep Neural Networks for Imbalanced Fault Classification of Machinery Using Generative Adversarial Networks

Mechanical fault datasets are always highly imbalanced with abundant common mechanical fault samples but a paucity of samples from rare fault conditions. To overcome this weakness, the simulation of rare fault signals is proposed in this paper. Specifically, frequency spectra are employed as model s...

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Bibliographic Details
Main Authors: Jinrui Wang, Shunming Li, Baokun Han, Zenghui An, Huaiqian Bao, Shanshan Ji
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Fault diagnosis
imbalanced classification
Wasserstein generative adversarial network (WGAN)
stacked autoencoders (SAE)
artificial signals
Online Access:https://ieeexplore.ieee.org/document/8742570/

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