Papers
22
Total Citations
573
H-Index
14
About
Heung Soo Kim is a distinguished researcher whose work spans robotics, prognostics and health management (PHM), and intelligent fault detection systems. Over his career, Kim has made pioneering contributions to the application of machine learning and deep learning for diagnosing mechanical faults in industrial robotic components, particularly RV reducers, strain wave gear reducers, and servo motor bearings. His data-driven methodologies — leveraging electrical current signature analysis, vibration data, and transfer learning — have redefined how the robotics industry approaches predictive maintenance and component-level health monitoring. Kim's most cited work, a 2022 study on machine learning-based fault detection for RV reducers (86 citations), exemplifies his ability to bridge advanced computational methods with real-world industrial challenges. His comprehensive 2023 review of deep learning applications in rotating machinery PHM (43 citations) has become an important reference for researchers entering the field. Earlier in his career, Kim also contributed foundational work in multi-agent robotic systems and omnidirectional mobile robotics, demonstrating remarkable breadth across decades of research. With a body of work accumulating hundreds of citations, Kim's research continues to shape the future of smart manufacturing and Industry 4.0 robotics infrastructure.
Research Focus
Key Achievements
Top Papers
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- 8Omnidirectional mobile base OK-II32 citations · 2002
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- 10Action selection mechanism for soccer robot28 citations · 2002