Yeong Rim Noh
Papers
2
Total Citations
40
H-Index
2
About
Yeong Rim Noh is a leading researcher in intelligent fault diagnosis and prognostics for robotic systems, with a primary focus on the strain wave gear reducer—a critical yet failure-prone component in modern robotics. Her work addresses the pressing challenge of monitoring machinery under nonstationary, variable-speed operations, where traditional vibration and acoustic emission techniques fall short. Noh’s major contributions include pioneering a data-driven deep learning approach for prognostic health management, enabling real-time prediction of reducer failures regardless of operational speed changes. Her 2022 paper on this topic has garnered 36 citations, reflecting its impact on advancing predictive maintenance in robotics. She further refined diagnostic accuracy in her 2023 work by introducing an area-metric-based sampling method to tackle the common issue of imbalanced data in deep learning fault diagnosis. This innovation enhances the reliability of intelligent systems under real-world conditions. Noh’s research is instrumental in transitioning robotic maintenance from reactive to proactive, offering significant cost and safety benefits for industrial automation. Her work is essential reading for engineers and researchers seeking robust, data-driven solutions for complex mechanical system health management.
Research Focus
Key Achievements
Top Papers
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