Yuxing Dai
Papers
2
Total Citations
12
H-Index
2
About
Yuxing Dai is a researcher whose work bridges advanced manufacturing and intelligent fault diagnosis, with a particular focus on industrial robotics and automation systems. His key research areas include few-shot learning for fault diagnosis, harmonic reducer reliability, and flexible manufacturing system design. Dai's most notable contribution is the development of the TCIFMN (Triplet Convolutional Inference Fusion Memory Network) framework for few-shot fault diagnosis of harmonic reducers in industrial robots, a 2024 paper that has already garnered 8 citations for addressing the critical challenge of limited fault data in complex working conditions. This work is particularly significant as it enables deep learning models to perform accurate diagnosis with minimal training samples, directly tackling a bottleneck in industrial maintenance. Earlier in his career, Dai contributed to manufacturing system architecture with his 1997 paper on integrated manufacturing systems, which addressed Hong Kong's shift from fixed to flexible automation in response to rising labor costs and demand for small-batch, high-variety production. His research trajectory demonstrates a sustained commitment to solving real-world industrial challenges through innovative computational and systems-level approaches.
Research Focus
Key Achievements
Top Papers
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