Intelligent rubbing fault identification using multivariate signals and a multivariate one-dimensional convolutional neural network-Reference-Cited by-同舟云学术

Intelligent rubbing fault identification using multivariate signals and a multivariate one-dimensional convolutional neural network

Published:2022-07 Issue: Volume:198 Page:116868
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Prosvirin Alexander E.,Maliuk Andrei S.,Kim Jong-Myon

Funder

Korea Ministry of Small and Medium Enterprises and Startups

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference40 articles.

1. Fast computation of the kurtogram for the detection of transient faults;Antoni;Mechanical Systems and Signal Processing,2007

2. Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks;Appana;Soft Computing,2018

3. Diagnosis of bearing defects in induction motors using discrete wavelet transform;Bessous;International Journal of System Assurance Engineering and Management,2018

4. Local rub-impact fault diagnosis of the rotor systems based on EMD;Cheng;Mechanism and Machine Theory,2009

5. Fault feature extraction of a rotor system based on local mean decomposition and Teager energy kurtosis;Deng;Journal of Mechanical Science and Technology,2014

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