Fangdi Ye | Offshore Wind Power | Research Excellence Award

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Mr. Fangdi Ye | Offshore Wind Power | Research Excellence Award

Zhejjiang Provincial Energy Group Company Ltd | China

Mr. Fundi is an accomplished energy-sector professional associated with Zhejiang Provincial Energy Group Company Ltd., where he contributes to the advancement of China’s offshore wind power initiatives. He holds an academic background in engineering and renewable energy management, building a solid foundation for his work in large-scale clean-energy development. Throughout his career, he has gained extensive experience in project planning, offshore infrastructure deployment, and strategic energy transition programs, working on initiatives that support regional sustainability goals and national carbon-neutral ambitions. His professional interests include offshore turbine technology, grid integration, marine environmental impact assessment, and the long-term scalability of renewable energy systems. Mr. Fundi has participated in collaborative research efforts with academic and industry partners, focusing on innovation in offshore wind operations and maintenance. He has been recognized within the sector for his contributions to technology modernization and efficiency improvements in coastal wind projects. His ongoing work reflects a commitment to accelerating the deployment of clean, reliable energy resources while promoting responsible environmental stewardship. Overall, Mr. Fundi’s career illustrates a forward-looking vision centered on renewable-energy innovation and the strategic development of China’s offshore wind power capabilities.

Profiles : Scopus | Orcid

Featured Publications

Xiao, T., Lian, J., Ye, F., Xiong, D., & Hao, X. (2024). Experimental and numerical study on motion characteristics of a bucket foundation during immersion process. Ocean Engineering. https://doi.org/10.1016/j.oceaneng.2024.117782

Ye, F., Lian, J., Xiao, T., Xiong, D., Wang, H., Guo, Y., & Shao, N. (2024). Behavior Analysis of a Bucket Foundation with an Offshore Wind Turbine during the In-Water Sinking Process. Journal of Marine Science and Engineering. https://doi.org/10.3390/jmse12030494

Ye, F., Lian, J., Xiong, D., Wang, H., Guo, Y., Wang, P., & Xiao, T. (2023). Effects of damping plate on the motion response of transport ships under waves. Applied Ocean Research. https://doi.org/10.1016/j.apor.2023.103507

Lian, J., Ye, F., Wang, P., Guo, Y., Wang, H., Xiao, T., & Xiong, D. (2023). Integrated transportation of offshore wind turbine and bucket foundation based on a U and K shaped assembled platform. Ocean Engineering. https://doi.org/10.1016/j.oceaneng.2023.114096

Ye, F.-D., Lian, J.-J., Guo, Y.-H., Wang, H.-J., Xiao, T.-R., Xiong, D.-Z., & Yu, T.-S. (2023). Stability Study on the Bucket Foundation with Multi-Compartment During the Floating-up Process Considering Air Compressibility. China Ocean Engineering. https://doi.org/10.1007/s13344-023-0081-8

Xiaoxia Yu | Data Analysis Innovation |  Best Scholar Award  

Published on by Research Data Analysis

Mr. Xiaoxia Yu | Data Analysis Innovation |  Best Scholar Award  

Chongqing University of Technology | China

Dr. Xiaoxia Yu is a scholar in mechanical engineering whose work advances intelligent diagnostics and predictive maintenance for large-scale rotating machinery, particularly wind turbines. With a Ph.D. in Mechanical Engineering and earlier degrees in Vehicle Engineering and Armored Vehicle Engineering, she has built a strong interdisciplinary foundation that integrates mechanical systems knowledge with advanced computational modeling. Her research spans fault diagnosis, health assessment, digital twin systems, graph neural networks, reinforcement learning, and signal processing, supported by a growing publication record that includes 29 documents, 477 citations by 448 documents, and an h-index of 7. As a Lecturer, she leads research projects funded by regional scientific agencies and has contributed to national-level R&D initiatives related to machinery health management. Her work appears in high-impact journals, and she has secured patents focused on structural health monitoring, image recognition, and intelligent fault detection. Recognized with competitive grants and academic honors, she continues to influence the fields of renewable energy reliability and smart manufacturing. Through her commitment to innovation, research leadership, and engineering application, she is emerging as a key contributor to the development of intelligent, data-driven mechanical health monitoring systems.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Yu, X., Tang, B., & Zhang, K. (2021). Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks. IEEE Transactions on Instrumentation and Measurement, 70, 1–14.

Yu, X., Tang, B., & Deng, L. (2023). Fault Diagnosis of Rotating Machinery Based on Graph Weighted Reinforcement Networks Under Small Samples and Strong Noise. Mechanical Systems and Signal Processing, 186, 109848.

Zhang, K., Tang, B., Deng, L., & Yu, X. (2021). Fault Detection of Wind Turbines by Subspace Reconstruction‑Based Robust Kernel Principal Component Analysis. IEEE Transactions on Instrumentation and Measurement, 70, 1–11.

Li, B., Tang, B., Deng, L., & Yu, X. (2020). Multiscale Dynamic Fusion Prototypical Cluster Network for Fault Diagnosis of Planetary Gearbox Under Few Labeled Samples. Computers in Industry, 123, 103331.

Xiong, P., Tang, B., Deng, L., Zhao, M., & Yu, X. (2021). Multi‑block Domain Adaptation with Central Moment Discrepancy for Fault Diagnosis. Measurement, 169, 108516.