Young Hun Jeong
Papers
1
Total Citations
84
H-Index
1
About
Young Hun Jeong is a leading researcher in intelligent manufacturing and machine health monitoring, with a particular focus on anomaly detection for industrial robotics. His most cited work, "Autoencoder-based anomaly detection of industrial robot arm using stethoscope based internal sound sensor" (2021, 84 citations), exemplifies his innovative approach to predictive maintenance. Jeong pioneered the use of acoustic sensing—specifically, a stethoscope-based internal sound sensor—combined with deep learning autoencoders to detect subtle mechanical faults in robotic arms before they lead to costly failures. This work has significant implications for smart factories, enabling non-invasive, real-time health diagnostics without disrupting production. Beyond this flagship study, his broader research spans sensor fusion, signal processing, and condition-based maintenance for cyber-physical systems. Jeong’s contributions are highly cited, reflecting their practical impact on reducing downtime and extending equipment life in automated manufacturing. His integration of low-cost acoustic sensors with advanced neural networks offers a scalable, accessible solution for industry, positioning him as a key figure in the transition toward self-aware, resilient production systems.
Research Focus
Key Achievements
Top Papers
- 1