Deep transfer learning for rolling bearing fault diagnosis under variable operating conditions

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...

Full description

Bibliographic Details
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)

Fault Diagnosis of Rolling Bearing Based on Modified Deep Metric Learning Method
by: Zengbing Xu, et al.
Published: (2021-01-01)

Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals
by: Hongmei Liu, et al.
Published: (2016-01-01)

Rolling Bearing Fault Diagnosis Using Improved Deep Residual Shrinkage Networks
by: Zhijin Zhang, et al.
Published: (2021-01-01)

Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder
by: Hongmei Shi, et al.
Published: (2020-10-01)

Deep Convolutional and LSTM Recurrent Neural Networks for Rolling Bearing Fault Diagnosis Under Strong Noises and Variable Loads
by: Meiying Qiao, et al.
Published: (2020-01-01)

Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition
by: Shiyuan Liu, et al.
Published: (2020-01-01)

Research on a Rolling Bearing Fault Detection Method With Wavelet Convolution Deep Transfer Learning
by: Mengliang Liao, et al.
Published: (2021-01-01)

Fault Diagnosis of Rolling Bearings Based on EWT and KDEC
by: Mingtao Ge, et al.
Published: (2017-12-01)

A new fault feature for rolling bearing fault diagnosis under varying speed conditions
by: Yong Ren, et al.
Published: (2017-06-01)

Rolling Bearing Fault Diagnosis Based on Domain Adaptation and Preferred Feature Selection under Variable Working Conditions
by: Xiao Yu, et al.
Published: (2021-01-01)

Lightweight Convolutional Neural Network and Its Application in Rolling Bearing Fault Diagnosis under Variable Working Conditions
by: Hengchang Liu, et al.
Published: (2019-11-01)

Rolling Bearing Fault Diagnosis under Variable Conditions Using Hilbert-Huang Transform and Singular Value Decomposition
by: Hongmei Liu, et al.
Published: (2014-01-01)

A Rolling Bearing Fault Diagnosis Method Based on EMD and Quantile Permutation Entropy
by: Qiang-qiang Chen, et al.
Published: (2019-01-01)

Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
by: Yong Ren, et al.
Published: (2019-03-01)

Application of SVM Optimized by IPSO in Rolling Bearing Fault Diagnosis
by: Lv Mingzhu, et al.
Published: (2018-01-01)

Weak fault diagnosis of rolling bearing based on FRFT and DBN
by: Xing He, et al.
Published: (2020-01-01)

An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing
by: Meijiao Li, et al.
Published: (2014-11-01)

Local coordinate weight reconstruction for rolling bearing fault diagnosis
by: Chenxi Wu, et al.
Published: (2020-11-01)

Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform
by: Bin Pang, et al.
Published: (2018-04-01)

A rolling bearing fault diagnosis method based on LSSVM
by: Xuejin Gao, et al.
Published: (2020-01-01)

Application of MCKD and improved LSSVM in fault diagnosis of rolling bearing
by: Liu Bo, et al.
Published: (2018-07-01)

Fault diagnosis of rolling bearing using CVA based detector
by: Baoxiang Wang, et al.
Published: (2016-11-01)

Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis
by: Jinde Zheng, et al.
Published: (2014-01-01)

Rolling Bearing Fault Diagnosis Based on the Coherent Demodulation Model
by: Yinghua Shao, et al.
Published: (2020-01-01)

Multi-Bandwidth Mode Manifold for Fault Diagnosis of Rolling Bearings
by: Tao Jiang, et al.
Published: (2019-01-01)

Enhanced Lightweight Multiscale Convolutional Neural Network for Rolling Bearing Fault Diagnosis
by: Yaowei Shi, et al.
Published: (2020-01-01)

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings
by: David Verstraete, et al.
Published: (2017-01-01)

Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis
by: Jun He, et al.
Published: (2021-01-01)

A Novel Deep Learning System with Data Augmentation for Machine Fault Diagnosis from Vibration Signals
by: Qiang Fu, et al.
Published: (2020-08-01)

An Intelligent Fault Diagnosis Scheme Using Transferred Samples for Intershaft Bearings Under Variable Working Conditions
by: Ya He, et al.
Published: (2020-01-01)

Rolling Bearing Fault Diagnosis Based on SVDP-Based Kurtogram and Iterative Autocorrelation of Teager Energy Operator
by: Bin Pang, et al.
Published: (2019-01-01)

Sequential fault detection for sealed deep groove ball bearings of in-wheel motor in variable operating conditions
by: Hongtao Xue, et al.
Published: (2017-12-01)

Rolling Bearing Fault Diagnosis Using a Deep Convolutional Autoencoding Network and Improved Gustafson–Kessel Clustering
by: Yaochun Wu, et al.
Published: (2020-01-01)

Input Feature Mappings-Based Deep Residual Networks for Fault Diagnosis of Rolling Element Bearing With Complicated Dataset
by: Liangsheng Hou, et al.
Published: (2020-01-01)

Size and Location Diagnosis of Rolling Bearing Faults: An Approach of Kernel Principal Component Analysis and Deep Belief Network
by: Heli Wang, et al.
Published: (2021-05-01)

Cannot write session to /tmp/vufind_sessions/sess_ip0ppu3v6436fpco255hbrvs02