Papers
1
Total Citations
3
H-Index
1
About
RuiQi Ruan’s research focuses on the intersection of industrial robotics, signal processing, and intelligent control, with a particular emphasis on enhancing the accuracy and reliability of robotic systems through innovative sensing and machine learning techniques. Their most cited work, "Action Recognition Method for Multi-joint Industrial Robots Based on End-arm Vibration and BP Neural Network" (2021), introduces a novel approach that leverages end-arm vibration signals and a Back Propagation neural network to recognize and classify the motion of multi-joint robots. This method directly addresses a critical challenge in industrial automation: linking test signals to specific joint movements to improve state evaluation and system precision. By integrating vibration analysis with neural network-based pattern recognition, Ruan’s contribution offers a practical, non-intrusive solution for real-time robot monitoring and diagnostics. Although the paper has garnered 3 citations to date, its impact is notable for pioneering a cost-effective, data-driven pathway to enhance robotic motion analysis. Ruan’s work is particularly valuable for researchers and engineers in robotics and manufacturing, providing a foundation for further exploration into sensor fusion and AI-driven industrial automation.
Research Focus
Key Achievements
Top Papers
- 1