Jinoh Yoo
Papers
1
Total Citations
8
H-Index
1
About
Jinoh Yoo is a researcher advancing the field of intelligent fault diagnosis for industrial robotics, with a focus on sound-based monitoring systems. His key research areas include deep learning for acoustic signal processing, generative adversarial networks (GANs), and data augmentation for predictive maintenance. Yoo’s most notable contribution is the development of a frequency-focused sound data generator for fault diagnosis in industrial robot reducers, published in 2024. This work introduces a conditional GAN that automatically selects target frequency ranges without requiring expert domain knowledge, enabling more accessible and efficient in situ fault detection. Despite its recent publication, the paper has already garnered 8 citations, reflecting its timely impact on the growing need for data-driven maintenance solutions in smart manufacturing. Yoo’s approach addresses a critical bottleneck—limited fault sound datasets—by generating realistic, frequency-targeted acoustic signals. His work is particularly valuable for students and researchers exploring the intersection of industrial automation and AI, offering a practical pathway to deploy robust diagnostic systems in real-world factory settings.
Research Focus
Key Achievements
Top Papers
- 1