发表论文 |
部分代表性论文如下： [1] J. Zhou, S. Li, J. Guo, L. Wang, Z. Liu, and T. Jin*, “Continuous hierarchical symbolic deviation entropy: A more robust entropy and its application to rolling bearing fault diagnosis,” Mechanical Systems and Signal Processing, vol. 227, Mar 15, 2025. (中科院一区，Top) [2] T. Jin, C. Chen, J. Guo, Z. Liu, and Y. Zhang, “Double-classifier adversarial learning for fault diagnosis of rotating machinery considering cross domains,” Mechanical Systems and Signal Processing, vol. 216, Jul 1, 2024. (中科院一区，Top) [3] X. Huang, T. Jin*, Z. Liu, C. Chen, C. Hua, and L. Zhang, “An interpretable multi-superlet kernel fusion convolutional neural network for rotating machinery fault diagnosis,” Expert Systems with Applications, pp. 128484, 2025. (中科院一区，Top) [4] K. Jiang, Z. Yang, T. Jin*, C. Chen, Z. Liu, and B. Zhang, “CNN-Based Rolling Bearing Fault Diagnosis Method With Quantifiable Interpretability,” IEEE Transactions on Instrumentation and Measurement, 2025. [5] J. Zhou, L. Wang, C. Chen, T. Jin*, J. Guo, Z. Wang, and C. Feng, “Fault Diagnosis Method of Rolling Bearing Based on Weighted Fractional Range Entropy Infogram,” IEEE Transactions on Instrumentation and Measurement, vol. 73, 2024, 2024. [6] T. Jin, C. Yan, C. Chen, Z. Yang, H. Tian, and S. Wang, “Light neural network with fewer parameters based on CNN for fault diagnosis of rotating machinery,” Measurement, vol. 181, Aug, 2021. |
