Ye Sheng Koh

Papers

Total Citations

H-Index

About

Ye Sheng Koh is a researcher focused on the intersection of robotics, automation, and intelligent maintenance systems, with key contributions in fault detection, augmented reality (AR), and sensor fusion. His work addresses critical challenges in Industry 4.0, particularly in enhancing the reliability and maintainability of automated machinery. Koh's most cited paper, "Development of AGV as Test Bed for Fault Detection" (2020, 6 citations), introduces an Automated Guided Vehicle (AGV) platform designed to simulate fault conditions and generate sensor data, providing a foundational tool for predictive maintenance research. He also co-authored "A Review on Augmented Reality Tracking Methods for Maintenance of Robots" (2020, 5 citations), which systematically evaluates AR tracking techniques for robot maintenance, bridging the gap between virtual guidance and physical repair tasks. More recently, his work on "Automated Guided Vehicle Robot Localization with Sensor Fusion" (2022, 2 citations) advances localization accuracy by integrating multiple sensor inputs, a critical step for autonomous navigation in dynamic environments. Through these contributions, Koh demonstrates a commitment to practical, data-driven solutions that improve machine uptime and operational efficiency, making his research valuable for students and engineers exploring fault diagnostics, AR-assisted maintenance, and multi-sensor systems in modern manufacturing.