An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing-Reference-Cited by-同舟云学术

An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing

Published:2020-10 Issue: Volume:162 Page:107901
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Gai Jingbo,Shen Junxian,Hu Yifan,Wang He

Funder

Harbin Engineering University

Publisher

Elsevier BV

Subject

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

Reference40 articles.

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2. An improved VMD with empirical mode decomposition and its application in incipient fault detection of rolling bearing;Fan;IEEE Access,2018

3. Rolling bearing fault diagnosis based on an improved HTT transform;Pang;Sensors,2018

4. Health degradation monitoring and early fault diagnosis of a rolling bearing based on CEEMDAN and improved MMSE;Lv;Materials,2018

5. Si-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis;Saidi;ISA Trans.,2014

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