Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network-Reference-Cited by-同舟云学术

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Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network

Published:2020-05 Issue: Volume:156 Page:107585
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Zhang Dingcheng,Stewart Edward,Entezami Mani,Roberts Clive,Yu Dejie

Funder

China Scholarship Council

National Natural Science Foundation of China

Guangzhou Science and Technology Plan

Publisher

Elsevier BV

Subject

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

Reference28 articles.

1. Acoustic performance of a semi-closed noise barrier installed on a high-speed railway bridge: measurement and analysis considering actual service conditions;Zhang;Measurement,2019

2. Detection of gear failures via vibration and acoustic signals using wavelet transform;Baydar;Mech. Syst. Sig. Process.,2003

3. Fault diagnosis of single-phase induction motor based on acoustic signals;Glowacz;Mech. Syst. Sig. Process.,2019

4. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals;Glowacz;Measurement,2018

5. Doppler Effect removal based on instantaneous frequency estimation and time domain re-sampling for wayside acoustic defective bearing detector system;Wang;Measurement,2014

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