Xinyang Cui
Papers
1
Total Citations
8
H-Index
1
About
Xinyang Cui is a leading researcher in intelligent fault diagnosis and mobile robotics, with a focus on addressing data scarcity and imbalance in complex industrial systems. Their most cited work introduces a novel **multimodal knowledge and multi-channel correlation generative adversarial network (MKC-GAN)** for generating realistic fault signals for mobile robots. This contribution directly tackles the critical challenge of imbalanced fault data, which often hampers the effectiveness of traditional diagnostic models. By effectively balancing the differences and correlations among multi-sensor channels, Cui’s method enables more robust and accurate fault detection, achieving 8 citations since its 2025 publication. This work represents a significant step forward in leveraging generative AI for predictive maintenance and autonomous system reliability. Cui’s research bridges deep learning, signal processing, and robotics, offering practical solutions for real-world industrial applications. Their innovative approach to multimodal data generation is poised to influence future studies in fault diagnosis and resilient autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1