论文： |
(1)Yang T, Tang T*, Wang J, et al. A novel cross-domain fault diagnosis method based on model agnostic meta-learning[J]. Measurement, 2022, 199: 111564.
(2)Tang T#, Wu J, Jun Z, et al. Lightweight model based two-step finetuning for fault diagnosis with limited data[J]. Measurement Science and Technology, 2022.
(3)Zhao J, Tang T*, Yu Y, et al. Adaptive Meta Transfer Learning with Efficient Self-Attention for Few-Shot Bearing Fault Diagnosis[J]. Neural Processing Letters, 2022: 1-20.
(4)Yu Y, Zhao J, Tang T*, et al. Wasserstein distance-based asymmetric adversarial domain adaptation in intelligent bearing fault diagnosis[J]. Measurement Science and Technology, 2021, 32(11): 115019.
(5) Wu J, Tang T*, Chen M, et al. A study on adaptation lightweight architecture based deep learning models for bearing fault diagnosis under varying working conditions[J]. Expert Systems with Applications, 2020, 160: 113710.
(6) Tang Tang#*, Hu Tianhao, Chen Ming, Lin Ronglai, Chen Guorui. A deep convolutional neural network approach with information fusion for bearing fault diagnosis under different working conditions[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2020: 0954406220902181.
(7) Hu Tianhao, Tang Tang*, Lin Ronglei, Chen Ming, Han Shufa, Wu Jie. A simple data augmentation algorithm and a self-adaptive convolutional architecture for few-shot fault diagnosis under different working conditions[J]. Measurement, 2020, 156: 107539.
(8) Hu Tianhao, Tang Tang*, Chen Ming Data Simulation by Resampling—A Practical Data Augmentation Algorithm for Periodical Signal Analysis-Based Fault Diagnosis[J]. IEEE Access, 2019, 7: 125133-125145.
(9) Wu Jie, Tang Tang*, Chen Ming, Hu Tianhao. Self-adaptive spectrum analysis based bearing fault diagnosis[J]. Sensors, 2018, 18(10): 3312.
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