Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

Rolling mill multi-row bearings are subjected to axial loads, which cause damage of rolling elements and cages, so the axial vibration signal contains rich fault character information. The vertical shock caused by the failure is weakened because multiple rows of bearings are subjected to radial forc...

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Bibliographic Details
Main Authors: Chen Zhao, Jianliang Sun, Shuilin Lin, Yan Peng
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Sensors
Subjects:
Adaptive Multivariate Variational Mode Decomposition
Multi-channel One-Dimensional Convolutional Neural Network
deep convolutional generation adversarial network
unbalanced dataset fault diagnosis
rolling mill multi-row bearings
Online Access:https://www.mdpi.com/1424-8220/21/16/5494

Internet

https://www.mdpi.com/1424-8220/21/16/5494

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