Tianyu Gao
Orcid: 0000-0002-5722-9231Affiliations:
- Harbin Institute of Technology, School of Electronics and Information Engineering, Harbin, China
According to our database1,
Tianyu Gao
authored at least 16 papers
between 2020 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
An Incipient Fault Diagnosis Method Based on Complex Convolutional Self-Attention Autoencoder for Analog Circuits.
IEEE Trans. Ind. Electron., August, 2024
Circuits Syst. Signal Process., February, 2024
A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions.
Reliab. Eng. Syst. Saf., 2024
A multi-source domain information fusion network for rotating machinery fault diagnosis under variable operating conditions.
Inf. Fusion, 2024
2023
A fault location method based on ensemble complex spatio-temporal attention network for complex systems under fluctuating operating conditions.
Appl. Soft Comput., September, 2023
A Novel Fault Detection Model Based on Vector Quantization Sparse Autoencoder for Nonlinear Complex Systems.
IEEE Trans. Ind. Informatics, March, 2023
Appl. Intell., March, 2023
2022
Energy-Based Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Electro-Mechanical Systems.
IEEE Trans. Instrum. Meas., 2022
A novel convolutional neural network with interference suppression for the fault diagnosis of mechanical rotating components.
Neural Comput. Appl., 2022
2021
An Efficient Method for Monitoring Degradation and Predicting the Remaining Useful Life of Mechanical Rotating Components.
IEEE Trans. Instrum. Meas., 2021
A Novel Incipient Fault Diagnosis Method for Analog Circuits Based on GMKL-SVM and Wavelet Fusion Features.
IEEE Trans. Instrum. Meas., 2021
A novel fault diagnosis method for analog circuits with noise immunity and generalization ability.
Neural Comput. Appl., 2021
A Novel Fault Diagnosis Method for Analog Circuits Based on Conditional Variational Neural Networks.
Circuits Syst. Signal Process., 2021
A Dual-input Fault Diagnosis Model Based on Convolutional Neural Networks and Gated Recurrent Unit Networks for Analog Circuits.
Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2021
2020
A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional, Self-Normalizing Convolutional Neural Networks.
Sensors, 2020