Xu Li

Orcid: 0000-0002-7992-6732

Affiliations:
  • Northeastern University, State Key Laboratory of Rolling and Automation, Shenyang, China


According to our database1, Xu Li authored at least 14 papers between 2020 and 2022.

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

Timeline

Legend:

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Online presence:

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Bibliography

2022
Degradation Alignment in Remaining Useful Life Prediction Using Deep Cycle-Consistent Learning.
IEEE Trans. Neural Networks Learn. Syst., 2022

Interval prediction of bending force in the hot strip rolling process based on neural network and whale optimization algorithm.
J. Intell. Fuzzy Syst., 2022

Predicting hot-strip finish rolling thickness using stochastic configuration networks.
Inf. Sci., 2022

2021
Open-Set Domain Adaptation in Machinery Fault Diagnostics Using Instance-Level Weighted Adversarial Learning.
IEEE Trans. Ind. Informatics, 2021

Universal Domain Adaptation in Fault Diagnostics With Hybrid Weighted Deep Adversarial Learning.
IEEE Trans. Ind. Informatics, 2021

Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions.
Reliab. Eng. Syst. Saf., 2021

Federated learning for machinery fault diagnosis with dynamic validation and self-supervision.
Knowl. Based Syst., 2021

A Novel GPR-Based Prediction Model for Strip Crown in Hot Rolling by Using the Improved Local Outlier Factor.
IEEE Access, 2021

2020
Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning.
IEEE Trans. Ind. Informatics, 2020

FPGA-Based Implementation of Stochastic Configuration Networks for Regression Prediction.
Sensors, 2020

Partial transfer learning in machinery cross-domain fault diagnostics using class-weighted adversarial networks.
Neural Networks, 2020

Data alignments in machinery remaining useful life prediction using deep adversarial neural networks.
Knowl. Based Syst., 2020

Domain generalization in rotating machinery fault diagnostics using deep neural networks.
Neurocomputing, 2020

Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation.
Neurocomputing, 2020


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