Fault diagnosis of rotating machinery based on combination of Wasserstein generative adversarial networks and long short term memory fully convolutional network-Reference-Cited by-同舟云学术

Fault diagnosis of rotating machinery based on combination of Wasserstein generative adversarial networks and long short term memory fully convolutional network

Published:2022-03 Issue: Volume:191 Page:110826
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Li Yibing,Zou Weiteng,Jiang Li

Funder

Hubei Province Natural Science Foundation

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference38 articles.

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3. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals;Zhang;Sensors,2017

4. A survey of fault diagnosis and fault-tolerant techniques-Part i: fault diagnosis with model-based and signal-based approaches;Gao;IEEE Trans. Ind. Electron.,2015

5. Early fault diagnosis of rotating machinery based on wavelet packets-Empirical mode decomposition feature extraction and neural network;Bin;Mech. Syst. Signal Process.,2012

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