Xin Li

Orcid: 0000-0003-1131-5772

Affiliations:
  • Hunan University, State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Changsha, China


According to our database1, Xin Li authored at least 13 papers between 2019 and 2024.

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

Timeline

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Bibliography

2024
Dynamics simulation-driven fault diagnosis of rolling bearings using security transfer support matrix machine.
Reliab. Eng. Syst. Saf., March, 2024

Research on bearing fault diagnosis method based on SCVMD and CGLF under various rotating speeds.
Trans. Inst. Meas. Control, 2024

Weak Feature Extraction Method for Bearing Faults Under Low-Speed Heavy-Duty Conditions.
IEEE Access, 2024

2023
A Novel Coal-Gangue Recognition Method for Top Coal Caving Face Based on IALO-VMD and Improved MobileNetV2 Network.
IEEE Trans. Instrum. Meas., 2023

Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging.
Reliab. Eng. Syst. Saf., 2023

2022
Highly Efficient Fault Diagnosis of Rotating Machinery Under Time-Varying Speeds Using LSISMM and Small Infrared Thermal Images.
IEEE Trans. Syst. Man Cybern. Syst., 2022

A Fusion CWSMM-Based Framework for Rotating Machinery Fault Diagnosis Under Strong Interference and Imbalanced Case.
IEEE Trans. Ind. Informatics, 2022

High-accuracy gearbox health state recognition based on graph sparse random vector functional link network.
Reliab. Eng. Syst. Saf., 2022

Maximum margin Riemannian manifold-based hyperdisk for fault diagnosis of roller bearing with multi-channel fusion covariance matrix.
Adv. Eng. Informatics, 2022

2021
Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-Riemannian kernel ridge regression.
Reliab. Eng. Syst. Saf., 2021

Discriminative manifold random vector functional link neural network for rolling bearing fault diagnosis.
Knowl. Based Syst., 2021

2020
Symplectic interactive support matrix machine and its application in roller bearing condition monitoring.
Neurocomputing, 2020

2019
A novel deep stacking least squares support vector machine for rolling bearing fault diagnosis.
Comput. Ind., 2019


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