Online detection of bearing incipient fault with semi-supervised architecture and deep feature representation-Reference-Cited by-同舟云学术

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Online detection of bearing incipient fault with semi-supervised architecture and deep feature representation

Published:2020-04 Issue: Volume:55 Page:179-198
ISSN:0278-6125
Container-title:Journal of Manufacturing Systems
language:en
Short-container-title:Journal of Manufacturing Systems

Author:

Mao Wentao^ORCID,Tian Siyu,Fan Jingjing,Liang Xihui^ORCID,Safian Ali

Funder

ational Key R&D Program of China

ational Natural Science Foundation of China

hina Postdoctoral Science Foundation Special Support

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Hardware and Architecture,Software,Control and Systems Engineering

Reference46 articles.

1. An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings;Lei;Mech Syst Signal Process,2017

2. Application of bandwidth EMD and adaptive multiscale morphology analysis for incipient fault diagnosis of rolling bearings;Li;IEEE Trans Ind Electron,2017

3. Dual-scale cascaded adaptive stochastic resonance for rotary machine health monitoring;Zhao;J Manuf Syst,2013

4. Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator;Ma;ISA Trans,2018

5. Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis;Liu;Mech Syst Signal Process,2016

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1. Online health monitoring of rotating machine elements using statistical spectral distances;Structural Health Monitoring;2024-09-11

2. A verified training support vector machine in bearing fault diagnosis;Measurement Science and Technology;2024-08-20

3. An Unsupervised Framework Based on Dual-Domain Contrastive Learning and Tri-Indicator Joint Alarm Strategy for Early Fault Detection of Bearing;IEEE Sensors Journal;2024-08-15

4. A semi-supervised production scheduling method based on co-training deep neural network for smart shop floors;Computers & Industrial Engineering;2024-08

5. Graph Entropy-Based Early Change Detection in Dynamical Bearing Degradation Process;IEEE Internet of Things Journal;2024-07-01

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