Jun Zhu

Orcid: 0000-0003-2544-5084

Affiliations:
  • Northwestern Polytechnical University, Department of Civil Aviation, Xi'an, China
  • National University of Singapore, Department of industrial engineering, Singapore (PhD 2020)


According to our database1, Jun Zhu authored at least 14 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
ReShape: A Universal Data Processing Approach for Clustering Inhomogeneous and Weakly Separated Manifold Data.
IEEE Trans. Ind. Informatics, September, 2024

A New Incremental Learning for Bearing Fault Diagnosis Under Noisy Conditions Using Classification and Feature-Level Information.
IEEE Trans. Instrum. Meas., 2024

2023
A New Multisensor Partial Domain Adaptation Method for Machinery Fault Diagnosis Under Different Working Conditions.
IEEE Trans. Instrum. Meas., 2023

A New Multisource Domain Bearing Fault Diagnosis Method With Adaptive Dual-Domain Obfuscation Weighting Strategy.
IEEE Trans. Instrum. Meas., 2023

Bearing Fault Diagnosis Under Multisensor Fusion Based on Modal Analysis and Graph Attention Network.
IEEE Trans. Instrum. Meas., 2023

Rotating Machinery Fault Diagnosis Under Multiple Working Conditions via a Time-Series Transformer Enhanced by Convolutional Neural Network.
IEEE Trans. Instrum. Meas., 2023

2022
Cross-Domain Open-Set Machinery Fault Diagnosis Based on Adversarial Network With Multiple Auxiliary Classifiers.
IEEE Trans. Ind. Informatics, 2022

Adversarial Domain-Invariant Generalization: A Generic Domain-Regressive Framework for Bearing Fault Diagnosis Under Unseen Conditions.
IEEE Trans. Ind. Informatics, 2022

2021
Dynamic Joint Distribution Alignment Network for Bearing Fault Diagnosis Under Variable Working Conditions.
IEEE Trans. Instrum. Meas., 2021

A New Multiple Source Domain Adaptation Fault Diagnosis Method Between Different Rotating Machines.
IEEE Trans. Ind. Informatics, 2021

2020
Multi-scale deep intra-class transfer learning for bearing fault diagnosis.
Reliab. Eng. Syst. Saf., 2020

Multi-source Unsupervised Domain Adaptation for Machinery Fault Diagnosis under Different Working Conditions.
Proceedings of the 18th IEEE International Conference on Industrial Informatics, 2020

2019
Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network.
IEEE Trans. Ind. Electron., 2019

A New Deep Fusion Network for Automatic Mechanical Fault Feature Learning.
IEEE Access, 2019


  Loading...