Tianyu Gao | Fault Diagnosis | Excellence in Research Award

Dr. Tianyu Gao | Fault Diagnosis | Excellence in Research Award

Harbin Institute of Technology | China

Tianyu Gao is a researcher from China whose work centers on advancing intelligent operation and maintenance technologies through AI-driven methods, with strong contributions to multimodal information fusion, intelligent sensing, and autonomous decision-making for unmanned equipment. He completed comprehensive academic training in measurement and control, instrumentation, and information and communication engineering at the Harbin Institute of Technology, developing a solid foundation that supports his interdisciplinary research. His professional experience spans postdoctoral and grant-supported research focused on improving the reliability of high-performance marine and unmanned propulsion systems by exploring robust diagnostic models and real-time health assessment strategies based on transfer learning and dynamic monitoring. He has contributed notable publications in leading journals covering complex system fault diagnosis, spatiotemporal attention networks, multimodal fusion, and intelligent fault detection. His research skills include advanced machine learning, deep learning, intelligent sensing, signal processing, autonomous diagnostic modeling, and system health monitoring, complemented by strong experience in scholarly reviewing for major IEEE and domain-leading journals. He has served as a session chair at prominent international conferences and is an active member of several IEEE societies. Overall, his work reflects a commitment to advancing intelligent equipment technologies, fostering innovation in autonomous systems, and contributing impactful research to the global engineering community.

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Featured Publications

Junkang Zheng | Fault diagnosis | Best Researcher Award

Mr. Junkang Zheng | Fault diagnosis | Best Researcher Award

Zhejiang Industry Polytechnic College | China

Junkang Zheng is a dedicated teacher at Zhejiang Industry Polytechnic College, specializing in the integration of artificial intelligence with industrial applications, particularly in intelligent fault diagnosis and numerical simulation. He has built his academic training and professional experience around computational analysis and smart diagnostic systems, applying AI-driven models to enhance accuracy, efficiency, and predictive analysis in industrial fault detection. His work demonstrates strong engagement with intelligent diagnosis research, producing peer-reviewed publications that contribute to developing more reliable and automated maintenance systems. His research interests include artificial intelligence algorithms, simulation-based equipment monitoring, and data-driven fault prediction, reflecting a commitment to improving industrial safety and performance through advanced computational tools. He possesses research skills in machine learning, numerical modeling, algorithm optimization, data processing, and diagnostic model implementation, enabling him to contribute to innovative solutions in equipment fault analysis. Zheng has also showcased his innovative capabilities through multiple patent contributions, supporting the practical translation of AI-based diagnostic technologies. His research outputs and patents have earned citations and recognition for their relevance in intelligent industrial systems. Overall, Zheng exemplifies a researcher who combines theoretical expertise with applicable innovations, helping advance intelligent condition monitoring and strengthening the role of AI in engineering reliability and industrial development.

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Featured Publications

Zheng, J., Han, S., Xue, M., Hu, H., & Wu, M. (2025). Numerical simulation-based intelligent fault detection for rotary vector reducers with imbalanced classes. Results in Engineering.