Yongchao Zhang
Orcid: 0000-0001-5892-3391Affiliations:
- Northeastern University, School of Mechanical Engineering and Automatio, Shenyang, China
According to our database1,
Yongchao Zhang
authored at least 12 papers
between 2020 and 2024.
Collaborative distances:
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Bibliography
2024
Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit.
Reliab. Eng. Syst. Saf., February, 2024
LAFICNN: A Novel Convolutional Adaptive Fusion Framework for Fault Diagnosis of Rotating Machinery.
IEEE Trans. Instrum. Meas., 2024
Estimation and Utilization of the Geomagnetic Field Inhomogeneities Using the Relaxation Characteristics of the FID Signal in an Overhauser Magnetometer.
IEEE Trans. Instrum. Meas., 2024
2023
Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning.
Expert Syst. Appl., December, 2023
CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery.
Inf. Fusion, July, 2023
Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing.
Reliab. Eng. Syst. Saf., June, 2023
High-Precision Electrical Determination and Correction of Attitude Deviation for the Coil Vector Magnetometer.
IEEE Trans. Instrum. Meas., 2023
Intelligent Suppression of Non-Maneuvering Magnetic Interference of Aeromagnetic UAV.
IEEE Trans. Instrum. Meas., 2023
Global contextual multiscale fusion networks for machine health state identification under noisy and imbalanced conditions.
Reliab. Eng. Syst. Saf., 2023
2022
MMFNet: Multisensor Data and Multiscale Feature Fusion Model for Intelligent Cross-Domain Machinery Fault Diagnosis.
IEEE Trans. Instrum. Meas., 2022
2021
Joint Domain Alignment and Class Alignment Method for Cross-Domain Fault Diagnosis of Rotating Machinery.
IEEE Trans. Instrum. Meas., 2021
2020
An Intelligent Fault Diagnosis for Rolling Bearing Based on Adversarial Semi-Supervised Method.
IEEE Access, 2020