Intelligent bearing fault diagnosis method combining mixed input and hybrid CNN-MLP model-Reference-Cited by-同舟云学术

Intelligent bearing fault diagnosis method combining mixed input and hybrid CNN-MLP model

Published:2022-11 Issue: Volume:180 Page:109454
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Sinitsin V.,Ibryaeva O.,Sakovskaya V.,Eremeeva V.

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference44 articles.

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3. Rolling element bearing fault diagnosis using convolutional neural network and vibration image;Hoang;Cogn. Syst. Res.,2019

4. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data;Jia;Mech. Syst. Signal Process.,2016

5. An enhancement denoising autoencoder for rolling bearing fault diagnosis;Meng;Measurement,2018

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