Papers
14
Total Citations
555
H-Index
8
About
Jay Lee is a pioneering researcher whose work bridges mechanical engineering, artificial intelligence, and industrial robotics. His key research areas include prognostics and health management (PHM), machine fault diagnosis, and robotic systems. Lee’s major contributions lie in developing neural network-based models for measuring and predicting machine performance degradation, a field where he is widely recognized as a foundational figure. His highly cited 1996 paper on using neural networks to assess machine degradation has garnered 121 citations, while his 1993 work on pattern discrimination models for degradation analysis has 56 citations. In robotics, Lee led the first clinical trial of the Amigo™ remote catheter system, a groundbreaking study with 156 citations that demonstrated the feasibility of robotic-assisted cardiac ablation. He has also advanced fault detection methodologies using clustering and support vector data description, with papers accumulating 67 and 37 citations respectively. More recently, Lee has focused on fault prognosis in industrial robots under dynamic working conditions, publishing influential work on ball screw diagnosis using non-stationary motor current signals (36 citations). His career-long dedication to making machines self-aware of their health has had a lasting impact on manufacturing reliability and robotic safety.
Research Focus
Key Achievements
Top Papers
- 1
- 2Measurement of machine performance degradation using a neural network model121 citations · 1996
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- 5Fault detection in a network of similar machines using clustering approach37 citations · 2012
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- 8Apply force/torque sensors to robotic applications19 citations · 1987
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