Deep transfer learning for rolling bearing fault diagnosis under variable operating conditions
Rolling bearings are the vital components of rotary machines. The collected data of rolling bearing have strong noise interference, massive unlabeled samples, and different fault features. Thus, a deep transfer learning method is proposed for rolling bearings fault diagnosis under variable operating...
Main Authors: | Changchang Che, Huawei Wang, Qiang Fu, Xiaomei Ni |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2019-12-01
|
Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/1687814019897212 |
Similar Items
-
The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest
by: Xiwen Qin, et al.
Published: (2021-01-01) -
Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition
by: Bo Zhou, et al.
Published: (2016-01-01) -
Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition
by: Yujie Cheng, et al.
Published: (2017-05-01) -
Rolling Bearing Fault Diagnosis Using Time-Frequency Analysis and Deep Transfer Convolutional Neural Network
by: Zhihao Chen, et al.
Published: (2020-01-01) -
A Deep Transfer Nonnegativity-Constraint Sparse Autoencoder for Rolling Bearing Fault Diagnosis With Few Labeled Data
by: Xingqiu Li, et al.
Published: (2019-01-01)