Papers
1
Total Citations
18
H-Index
1
About
Ho-Jin Lee is a leading researcher in industrial robotics and intelligent fault diagnosis, with a focus on enhancing the reliability of automated systems. His work centers on developing deep learning-based methods for detecting faults in industrial robot control cables, a critical area for factory automation. Lee’s major contribution is a current-only based fault diagnosis approach that leverages novelty detection to address imbalanced fault datasets—a common challenge in real-world manufacturing where normal data vastly outnumbers faulty samples. His 2022 paper on this method has garnered 18 citations, reflecting its practical relevance and impact on predictive maintenance. By enabling automatic feature extraction and robust fault identification under diverse conditions, Lee’s research reduces downtime and improves safety in industrial settings. His work is particularly notable for bridging the gap between theoretical deep learning models and applied diagnostics, offering scalable solutions for smart factories. For students and researchers, Lee’s contributions exemplify how AI-driven techniques can solve pressing industrial challenges, making him a key figure in the evolution of intelligent manufacturing systems.
Research Focus
Key Achievements
Top Papers
- 1