Biography

Dr. Yadong Xu

The Hong Kong Polytechnic University, China


Email: [email protected]


Qualifications

2023 Ph.D., School of Mechanical Engineering, Southeast University, China

2023 Ph.D., School of Engineering, The University of British Columbia, Canada

2020 M.Sc., School of Mechanical Engineering, Southeast University, China

2016 B.Sc., School of Mechatronic Engineering, Nanjing Forestry University, China


Publications (Selected)

  1. Li, Z., Yan, Y., Zhao, Z., Xu, Y., & Hu, Y. (2025). Numerical study on hydrodynamic effects of intermittent or sinusoidal coordination of pectoral fins to achieve spontaneous nose-up pitching behavior in dolphins. Ocean Engineering, 337, 121854.
  2. Xu, Y., Li, S., Feng, K., Huang, R., Sun, B., Yang, X., ... & Huang, G. Q. (2025). Domain constrained cascadic multireceptive learning networks for machine health monitoring in complex manufacturing systems. Journal of Manufacturing Systems, 80, 563-577.
  3. Yan, X., Jiang, D., Xiang, L., Xu, Y., & Wang, Y. (2024). CDTFAFN: A novel coarse-to-fine dual-scale time-frequency attention fusion network for machinery vibro-acoustic fault diagnosis. Information Fusion, 112, 102554.
  4. Li, S., Ji, J., Feng, K., Zhang, K., Ni, Q., & Xu, Y. (2024). Composite neuro-fuzzy system-guided cross-modal zero-sample diagnostic framework using multi-source heterogeneous non-contact sensing data. IEEE Transactions on Fuzzy Systems.
  5. Li, S., Jiang, Q., Xu, Y., Feng, K., Zhao, Z., Sun, B., & Huang, G. Q. (2024). Digital twin-assisted interpretable transfer learning: A novel wavelet-based framework for intelligent fault diagnostics from simulated domain to real industrial domain. Advanced Engineering Informatics, 62, 102681.
  6. Li, S., Feng, K., Xu, Y., Li, Y., Ni, Q., Zhang, K., ... & Ding, W. (2024). Cross-modal zero-sample diagnosis framework utilizing non-contact sensing data fusion. Information Fusion, 110, 102453.
  7. Xu, Y., Jiang, Q., Li, S., Zhao, Z., Sun, B., & Huang, G. Q. (2024). Digital twin-driven discriminative graph learning networks for cross-domain bearing fault recognition. Computers & Industrial Engineering, 193, 110292.
  8. He, J., Xu, Y., Pan, Y., & Wang, Y. (2024). Adaptive weighted generative adversarial network with attention mechanism: A transfer data augmentation method for tool wear prediction. Mechanical Systems and Signal Processing, 212, 111288.
  9. Li, S., Ji, J. C., Xu, Y., Feng, K., Zhang, K., Feng, J., ... & Wang, Y. (2024). Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults. Mechanical Systems and Signal Processing, 210, 111142.
  10. Xu, Y., Li, S., Yan, X., He, J., Ni, Q., Sun, Y., & Wang, Y. (2024). Multiattention-based feature aggregation convolutional networks with dual focal loss for fault diagnosis of rotating machinery under data imbalance conditions. IEEE Transactions on Instrumentation and Measurement, 73, 1-11.
  11. Li, S., Jiang, Q., Xu, Y., Feng, K., Wang, Y., Sun, B., ... & Ni, Q. (2023). Digital twin-driven focal modulation-based convolutional network for intelligent fault diagnosis. Reliability Engineering & System Safety, 240, 109590.
  12. Zhang, Y., Yu, K., Lei, Z., Ge, J., Xu, Y., Li, Z., ... & Feng, K. (2023). Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning. Expert Systems with Applications, 232, 120854.
  13. Xu, Y., Chen, Y., Zhang, H., Feng, K., Wang, Y., Yang, C., & Ni, Q. (2023). Global contextual feature aggregation networks with multiscale attention mechanism for mechanical fault diagnosis under non-stationary conditions. Mechanical Systems and Signal Processing, 203, 110724.
  14. Yan, X., Yan, W. J., Xu, Y., & Yuen, K. V. (2023). Machinery multi-sensor fault diagnosis based on adaptive multivariate feature mode decomposition and multi-attention fusion residual convolutional neural network. Mechanical Systems and Signal Processing, 202, 110664.
  15. Xu, Y., Ji, J. C., Ni, Q., Feng, K., Beer, M., & Chen, H. (2023). A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems. Mechanical Systems and Signal Processing, 200, 110609.


Profile Details

https://orcid.org/0000-0003-0713-4434

https://scholar.google.com/citations?user=5k9tgc4AAAAJ&hl=en

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