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Yong Chae Kim

Yong Chae Kim

Ph.D. Candidate · Mechanical Engineering · Seoul National University

My research focuses on deep learning for Prognostics and Health Management (PHM) in mechanical systems, including fault diagnosis, signal generation, and denoising. I work with domain adaptation, diffusion models, and physics-informed approaches. Currently, I am exploring manufacturing foundation models and agentic AI for industrial applications.

Outside of research, I enjoy traveling to new places, working on meaningful projects, and connecting with people.

— Journal Papers
— Int'l Conf.
— Domestic Conf.
— Awards
— Patents

Research Overview

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Selected Publications

Representative first-author journal articles. Full list on CV page →

J11
Physics-guided deep ensemble learning for the remaining useful life prediction of machine tools using kernel density estimation
Kim, Y.C., Kim, B., Kim, M., Lee, S.K., Jung, J.H., & Youn, B.D.
Int'l J. Precision Eng. and Manufacturing-Green Technology 2025 SCIE
J7
Latent Space Alignment based Domain Adaptation (LSADA) for Fault Diagnosis of Rotating Machinery
Kim, Y.C., Ko, J.U., Lee, J., Kim, T., Jung, J.H., & Youn, B.D.
Advanced Engineering Informatics 2024 SCIE
J5
Gradient Alignment based Partial Domain Adaptation (GAPDA) using a domain knowledge filter for fault diagnosis of bearing
Kim, Y.C., Lee, J., Kim, T., Baek, J., Ko, J.U., Jung, J.H., & Youn, B.D.
Reliability Engineering & System Safety 2024 SCIE
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Citation Trend

— Total Citations
— h-index
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Source: Google Scholar