Xueyi Li
Orcid: 0000-0003-0335-0594Affiliations:
- Northeast Forestry University, College of Mechanical and Electrical Engineering, Harbin, China
- Tsinghua University, Department of Mechanical Engineering, Beijing, China (former)
- Northeastern University, School of Mechanical Engineering and Automation, Shenyang, China (former)
According to our database1,
Xueyi Li
authored at least 12 papers
between 2019 and 2025.
Collaborative distances:
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Bibliography
2025
Digital twin-driven imbalanced fault diagnosis method based on new distribution discrepancy metric and large margin aware focal loss.
Simul. Model. Pract. Theory, 2025
Mixed style network based: A novel rotating machinery fault diagnosis method through batch spectral penalization.
Reliab. Eng. Syst. Saf., 2025
The bearing multi-sensor fault diagnosis method based on a multi-branch parallel perception network and feature fusion strategy.
Reliab. Eng. Syst. Saf., 2025
Fault diagnosis method for imbalanced data based on adaptive diffusion models and generative adversarial networks.
Eng. Appl. Artif. Intell., 2025
Hierarchical Convolution-Transformer Framework for Gear Fault Diagnosis Under Severe Noise.
IEEE Access, 2025
2024
Fusion innovation: Multi-scale dilated collaborative model of ConvNeXt and MSDA for fault diagnosis.
Robotics Auton. Syst., 2024
2021
Attention Recurrent Autoencoder Hybrid Model for Early Fault Diagnosis of Rotating Machinery.
IEEE Trans. Instrum. Meas., 2021
A Novel Framework for Early Pitting Fault Diagnosis of Rotating Machinery Based on Dilated CNN Combined With Spatial Dropout.
IEEE Access, 2021
An improved CNN based on attention mechanism with multi-domain feature fusion for bearing fault diagnosis.
Proceedings of the International IEEE Conference on Prognostics and Health Management, 2021
2020
Unsupervised rotating machinery fault diagnosis method based on integrated SAE-DBN and a binary processor.
J. Intell. Manuf., 2020
2019
Sensors, 2019
A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction.
IEEE Access, 2019