Guohong Dai
Papers
1
Total Citations
9
H-Index
1
About
Guohong Dai is a researcher whose work centers on robotics, fault diagnosis, and motion analysis, with a particular focus on enhancing the reliability and precision of industrial manipulators. Their major contribution lies in developing innovative diagnostic methods for robotic systems, most notably demonstrated in their highly cited work on fault diagnosis of selective compliance assembly robot arms (SCARA). In this study, Dai introduced a threshold algorithm based on end joint motion analysis, enabling effective detection of mechanical faults through signal processing and model parameter evaluation. This approach, detailed in their 2017 paper, has garnered 9 citations, reflecting its practical relevance in robotics maintenance and automation. By bridging motion signal analysis with diagnostic algorithms, Dai’s research offers a cost-effective, non-invasive solution for improving robotic system uptime and safety. Their work is particularly valuable for students and engineers exploring condition monitoring in industrial robotics, as it provides a clear methodology for translating motion data into actionable fault detection. Dai’s contributions underscore the importance of integrating signal processing with mechanical design to advance smart manufacturing and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1