Eui-Youl Kim
Papers
1
Total Citations
37
H-Index
1
About
Eui-Youl Kim is a researcher specializing in intelligent condition monitoring, fault diagnosis, and smart manufacturing, with a particular focus on applying advanced signal processing and machine learning techniques to industrial systems. His most-cited work, "Heath monitoring of a glass transfer robot in the mass production line of liquid crystal display using abnormal operating sounds based on wavelet packet transform and artificial neural network" (2012, 37 citations), exemplifies his core contribution: developing non-invasive, sound-based health monitoring methods for critical manufacturing equipment. By integrating wavelet packet transform with artificial neural networks, Kim pioneered a practical approach to detect anomalies in glass transfer robots—a key component in LCD production—using operational sounds, thereby reducing downtime and improving quality control. This work has been influential in the field of acoustic-based predictive maintenance, demonstrating how machine learning can transform raw acoustic data into actionable insights for industrial automation. Kim’s research bridges the gap between theoretical signal processing and real-world manufacturing challenges, offering scalable solutions for high-precision production environments. His contributions continue to inform the development of smarter, more resilient manufacturing systems, making him a notable figure in the intersection of AI and industrial engineering.
Research Focus
Key Achievements
Top Papers
- 1